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LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation

Yushi Lan, Fangzhou Hong, Shangchen Zhou, Shuai Yang, Xuyi Meng, Yongwei Chen, Zhaoyang Lyu, Bo Dai, Xingang Pan, Chen Change Loy

arXiv:2403.12019v3cs.CV

TL;DR

The paper addresses the lack of a unified 3D diffusion pipeline and challenges in scalability, efficiency, and conditional generation. It learns a compact 3D-aware latent with a VAE and performs conditional diffusion in that space. The authors report strong results across generation and reconstruction tasks, with faster inference than latent-free methods.

  • Problem

    A unified 3D diffusion pipeline remains unsettled, while existing methods face scalability, efficiency, and generalizability challenges.

    Source: §I · p. 1

  • Method

    A VAE encodes images into a compact 3D-aware latent, which a transformer-based decoder maps to a neural field for conditional diffusion learning.

    Source: §I · pp. 1–2, A. Perceptual 3D Latent Compression · p. 5

  • Results

    The authors report state-of-the-art ShapeNet generation, superior monocular reconstruction and conditional generation across datasets, and 3× faster inference than latent-free 3D methods.

    Source: §I · p. 2, §V · p. 11

  • Takeaways & Limitations

    The learned compact 3D-aware latent is presented as a basis for efficient diffusion learning and downstream 3D vision and graphics applications.

    Source: §V · p. 11, D. More Monocular 3D Reconstruction… · p. 15

    Because the VAE requires Plücker camera conditions, the pretrained method cannot be directly applied to unposed datasets.

    Source: E. VAE Limitations · p. 16

Abstract

from arXiv · show

The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff++ to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image into a structured, compact, and 3D latent space. The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field. Through training a diffusion model on this 3D-aware latent space, our method achieves state-of-the-art performance on ShapeNet for 3D generation and demonstrates superior performance in monocular 3D reconstruction and conditional 3D generation across various datasets. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Our proposed LN3Diff presents a significant advancement in 3D generative modeling and holds promise for various applications in 3D vision and graphics tasks.

I. INTRODUCTION

The introduction frames the lack of a unified 3D diffusion pipeline alongside scalability, efficiency, and generalizability challenges. LN3DIFF++ learns a compact 3D-aware latent for conditional generation and reports strong results across 3D generation, reconstruction, and conditional tasks.

I. INTRODUCTION

  • Existing 3D diffusion approaches face scalability, efficiency, and generalizability challenges, including costly data preparation, high-dimensional or unclean latents, and limited conditional generation.The introduction notes that some existing methods require over 50 views per instance and that projecting monocular images into diffusion latents is challenging.Source: §I · p. 1
  • LN3DIFF++ compresses input images into a compact 3D-aware latent with a VAE, then decodes it into a high-capacity neural field using a 3D-aware transformer.The autoencoding stage uses differentiable rendering, novel-view supervision for multi-view data, and adversarial supervision for monocular data.Source: §I · p. 1
  • LN3DIFF++ extends the earlier LN3Diff setup from NeRF and text-conditioned generation to SDF representation and image-conditioned generation.The introduction also describes monocular reconstruction and text-to-3D synthesis.Source: Figure 1 · p. 2
  • The extension replaces DDPM+U-Net with flow matching and DiT, and fine-tunes an SDF-based representation to support mesh extraction.The paper states that SDFs can be readily converged into meshes.Source: §I · p. 2
  • LN3DIFF++ reports state-of-the-art ShapeNet generation, superior monocular reconstruction and conditional generation across datasets, and 3× faster inference than latent-free 3D methods.The reported tasks span ShapeNet, FFHQ, and Objaverse.Source: §I · p. 2

II. RELATED WORK

Related work spans 2D diffusion adapted to 3D, native 3D diffusion, and generalized reconstruction. The paper contrasts these approaches by their optimization cost, representation constraints, task scope, and support for conditional generation.

II. RELATED WORK

  • 2D diffusion-based 3D generation can require costly optimization or lack view consistency and full 3D structure; multi-view pipelines also depend on generated-view quality and can constrain editing.The cited work includes score distillation, 2D-prior approaches, and multi-view generation followed by reconstruction.Source: §II · p. 3
  • Native 3D diffusion methods vary in input formats, latent structures, and output representations, while auto-decoder pipelines can yield unclean, large latents that hinder scalability and efficient diffusion.The passage gives 256×256×96 as an example of a large latent code.Source: §II · p. 3
  • Several native 3D methods remain constrained by costly fitting, single-category settings, or shape-only outputs, whereas LN3DIFF++ is described as supporting conditional generation across diverse datasets.The comparison also notes that RenderDiffusion and SSDNeRF focus on simpler classes, while some alternatives do not generate colorful meshes in one stage.Source: §II · p. 3
  • Prior image-based rendering methods are primarily designed for view synthesis and lack explicit 3D representations; VQ3D adapts generalized reconstruction to 3D generative models, using a 2D architecture with autoregressive modeling over a 1D latent space.Source: §II · p. 3

III. SCALABLE LATENT NEURAL FIELDS DIFFUSION

The method separates 3D compression from diffusion by learning a dedicated VAE and applying a diffusion prior in its compact latent space. This separation is presented as supporting representation-agnostic use, efficient learning, and reuse of the learned tokenizer.

III. SCALABLE LATENT NEURAL FIELDS DIFFUSION

  • A variational encoder maps posed images of an object to latent z, a denoiser predicts noise for a noisy latent, and a decoder maps z0 to a 3D tri-plane.The decoder comprises a Transformer and an upsampler.Source: §III · p. 4
  • Separating compression from diffusion avoids representation-specific 3D diffusion designs and allows the pipeline to work with different neural rendering techniques.The passage characterizes the resulting diffusion as 3D representation/rendering-agnostic.Source: §III · p. 4
  • Diffusion in the compressed latent reuses latent diffusion architectures for computationally efficient learning and faster sampling than learning in high-dimensional 3D space.The trained compression model also serves as a general-purpose tokenizer that can be reused for downstream applications or extended to new datasets.Source: §III · p. 4

A. Perceptual 3D Latent Compression

The compression stage maps posed images into a structured, KL-regularized 3D latent and decodes it with attention designed to exchange information within and across latent planes. Rendering, geometry, KL, and adversarial objectives train the reconstruction, with data and representation choices varying by setting.

A. Perceptual 3D Latent Compression

  • The encoder compresses posed image sets into a KL-regularized tri-latent, using Plücker camera coordinates and, for Objaverse, rendered depth as inputs.The latent has shape h×w×3×c, with down-sampling factor f=8 and three latent planes.Source: A. Perceptual 3D Latent Compression · p. 4
  • Self-plane attention aggregates features within each latent plane, while cross-plane attention lets tokens attend across planes for global information flow.The design is fully attention-based and supports parallel computing without Rodin's axis-pooling aggregation.Source: A. Perceptual 3D Latent Compression · p. 5
  • The decoder uses DiT blocks with latent-conditioned adaptive layer normalization, then reshapes transformer tokens and upsamples them into a tri-plane.For monocular datasets, input-view and auxiliary novel-view discriminators support adversarial supervision; using real images for the novel-view discriminator can cause posterior collapse.Source: A. Perceptual 3D Latent Compression · p. 5
  • The compression objective combines rendering, geometry regularization, KL regularization, and adversarial losses.Rendering loss mixes L1 and perceptual losses and applies to input and randomly sampled novel views.Source: A. Perceptual 3D Latent Compression · p. 5
  • The model is fine-tuned with Flexicubes and an additional loss for mesh extraction, updating only the 3D VAE decoder at that stage.The added terms include rendered-normal MAE and Flexicubes-parameter regularization.Source: A. Perceptual 3D Latent Compression · p. 5
  • Training uses one novel-view supervision for ShapeNet and four for diverse Objaverse, compared with 50 views required by cited prior methods.The paper describes its approach as more data-efficient than those methods.Source: A. Perceptual 3D Latent Compression · p. 5

B. Latent Diffusion and Denoising

LN3DIFF++ learns a latent prior that denoises compact 3D codes, then decodes generated samples into neural fields. The section describes both score-based latent diffusion and a flow-matching alternative.

B. Latent Diffusion and Denoising

  • The denoiser learns to reverse noise in a perceptual latent space, starting from a standard Normal prior.It parameterizes the score function and is trained to predict a denoised variant of the noisy input.Source: B. Latent Diffusion and Denoising · p. 5
  • A learnable geometric mixture moves denoiser inputs closer to a standard Normal distribution to support faster reverse-SDE solving.The mixture coefficient α is constrained to [0, 1].Source: B. Latent Diffusion and Denoising · p. 5
  • The training objective is conditioned on c, with t sampled uniformly from [0, 1] and a time-dependent empirical weight.After training, a single forward pass through Dψ decodes denoised samples into a 3D neural field for neural rendering.Source: B. Latent Diffusion and Denoising · pp. 5–6
  • Flow matching trains a DiT over full attention on compact tri-plane latents to predict noisy-input velocity along a straight-line trajectory.The forward path connects the data distribution to a Normal distribution; the section contrasts this with the U-Net's rolled tri-latent input.Source: B. Latent Diffusion and Denoising · p. 6

C. Conditioning Mechanisms

LN3DIFF++ conditions latent 3D diffusion on text or images using CLIP features, with DINO patch features added for image-conditioned generation on category-free data. Classifier-free guidance supports both conditional and unconditional sampling.

C. Conditioning Mechanisms

  • Text captions are encoded as 77 × 768 CLIP tokens and injected into the denoiser through cross-attention.CLIP image embeddings are also injected through cross-attention for image-conditioned generation.Source: C. Conditioning Mechanisms · p. 6
  • For category-free datasets such as Objaverse, DINO patch tokens enter self-attention and improve image-conditioned generation faithfulness and fidelity.The method prepends patch tokens rather than adding another cross-attention module.Source: C. Conditioning Mechanisms · p. 6
  • Classifier-free guidance trains conditional and unconditional settings by zeroing condition embeddings with 15% probability, then mixes their score estimates at sampling.The guidance scale s controls the mixing strength to balance sampling diversity and quality.Source: C. Conditioning Mechanisms · pp. 6–7
  • Fig. 4 shows four unconditional ShapeNet samples for each method.The caption recommends zooming in for the best view.Source: Figure 4 · p. 7

IV. EXPERIMENTS

Experiments use ShapeNet and Objaverse, with dataset-specific input views and rendering settings. Both the autoencoding and diffusion models are trained for 800K iterations.

IV. EXPERIMENTS

  • ShapeNet experiments cover Car, Chair, and Plane, with 3,514, 6,700, and 4,045 instances, respectively.Instances are randomly rendered from 50 views; Objaverse adds a high-quality subset of around 176K instances with 40 random views each.Source: §IV · pp. 7–8
  • Fig. 5 presents conditional ShapeNet generation using both text and image inputs.The caption recommends zooming in for the best view.Source: Figure 5 · p. 8
  • VAE training uses one input view for ShapeNet and FFHQ, but six posed RGB-D views for Objaverse.Inputs are 128 × 128 for ShapeNet and FFHQ and 256 × 256 for Objaverse; target renderings are 192 × 192 for Objaverse.Source: §IV · p. 8
  • The DiT-L generative model has 24 transformer layers, 16 attention heads, and 1,024 latent dimensions; both models train for 800K iterations.Training takes around seven days using eight A100 GPUs.Source: §IV · p. 8

A. Metrics and Baselines

The evaluation compares unconditional, text-conditioned, and image-conditioned 3D generation against task-specific baselines using rendering and geometry metrics. The text-to-3D comparison with GaussianCube V1.1 is explicitly identified as unfair because it uses additional data and different captions.

A. Metrics and Baselines

  • Unconditional ShapeNet baselines include GANs EG3D and GET3D, diffusion methods DiffRF, RenderDiffusion, and SSDNeRF, plus 3DShape2Vec for geometry comparisons.SSDNeRF is evaluated with both its official 50-view version and a reproduced 3-view version; it fails to converge with V = 2.Source: A. Metrics and Baselines · p. 8
  • Text-to-3D comparisons include Point-E, Shape-E, 3DTopia, and GaussianCube V1.1, while image-to-3D comparisons span single-image, multi-view, and native-diffusion methods.GaussianCube V1.1 is for reference only: it uses auxiliary data beyond Objaverse and proprietary-model captions rather than Cap3D, making the comparison unfair.Source: A. Metrics and Baselines · pp. 8–9
  • Rendering quality is measured with FID, KID, CLIP, and MUSIQ-koniq, while geometry is assessed with P-FID, P-KID, COV, and MMD.Unconditional-generation FID/KID and geometry metrics are computed at 128 × 128 resolution for fair baseline comparisons.Source: A. Metrics and Baselines · pp. 8–9
  • The text-to-3D table reports that LN3DIFF++ outperforms Point-E and Shape-E on CLIP scores across two backbones.The caption does not provide the underlying score values.Source: Table I · p. 9

B. Evaluation

The evaluation reports LN3DIFF++ results across unconditional and conditional 3D generation, monocular reconstruction, and image-to-3D reconstruction, with comparisons to GAN- and diffusion-based methods.

B. Evaluation

  • LN3DIFF++ achieves quantitatively better rendering quality and 3D coverage than GAN-based baselines on unconditional ShapeNet generation.The paper also reports higher visual quality and stronger quantitative performance than diffusion-based baselines, while SSDNeRF V=50 has better coverage than LN3DIFF++ with V=2.Source: B. Evaluation · p. 9, Table II · p. 10
  • For image-conditioned generation, LN3DIFF++ produces semantically similar samples while maintaining diversity.The evaluation also describes its conditional outputs as closely aligned with text or image prompts.Source: B. Evaluation · p. 9
  • For category-free text-conditioned generation, LN3DIFF++ reports better quality than Point-E, Shape-E, and 3DTopia, despite using fewer computational resources.GaussianCube V1.1 has sharper texture and higher aesthetic score, but was trained on proprietary data.Source: B. Evaluation · p. 9
  • On FFHQ, LN3DIFF++ reports an FID of 36.6 versus 59.3 for RenderDiffusion and supports conditional generation at 128 × 128 rather than 64 × 64.The qualitative comparison also reports high-fidelity monocular reconstructions that preserve semantic details in self-occluded images.Source: B. Evaluation · p. 9
  • On unseen GSO images, LN3DIFF++ produces intact reconstructions for complicated structures and self-occlusions, with more natural back views than regression-based methods.The authors report state-of-the-art CLIP-I and 3D metrics, with competitive FID/KID; LGM’s better FID/KID accompanies worse 3D geometry.Source: B. Evaluation · p. 9
  • The proposed method achieves state-of-the-art performance on CLIP-I and all 3D metrics, with competitive results on conventional 2D-rendering metrics FID/KID.LGM uses pre-trained MVDream as the first-stage generation and maps the generated four views to pixel-aligned 3D Gaussians; this cascaded pipeline achieves better visual quality but is prone to distorted 3D geometry.Source: B. Evaluation · p. 9

C. Ablation Study and Analysis

The supplied ablation material includes an auto-encoding architecture benchmark and qualitative results for FFHQ reconstruction and ShapeNet text-to-3D generation.

C. Ablation Study and Analysis

  • The auto-encoding architecture components are benchmarked on a 7K-instance Objaverse subset using PSNR.The passage identifies Tab. IV but does not provide its numerical results.Source: C. Ablation Study and Analysis · p. 10
  • The ShapeNet text-to-3D figure presents uncurated LN3DIFF++ samples from three categories, with two views shown for each sample.The nearby labels identify generation input and novel view.Source: Figure 6 · p. 10, C. Ablation Study and Analysis · p. 11
  • On FFHQ, the method is reported to perform better than the baseline for both monocular reconstruction and 3D generation.The figure evaluates reconstruction on a hold-out test set and shows input and novel views.Source: Figure 7 · p. 11

V. CONCLUSION AND DISCUSSIONS

The discussion summarizes LN3DIFF++’s generation and reconstruction results, describes limitations, and provides supplementary training, evaluation, and qualitative material.

V. CONCLUSION AND DISCUSSIONS

  • The paper presents diffusion over a compact, 3D-aware latent space as its generative modeling paradigm, reports state-of-the-art ShapeNet performance, and demonstrates the method on category-free Objaverse.The discussion also identifies downstream 3D vision and graphics applications as a potential use.Source: §V · p. 11
  • For unseen GSO images, the qualitative comparison reports stable performance across cases; FlexiCube produces high-quality normal renderings that are difficult to obtain from the NeRF version.The table caption reports strong image-conditioned results across metrics, while noting LGM’s FID/KID advantage and weaker CLIP-I and 3D shape quality.Source: Table III · p. 11, Figure 8 · p. 12
  • The reported ablations associate auto-encoding components with reconstruction gains, the novel-view discriminator with greater 3D consistency, and DINO features with improved fidelity and faithfulness.The DINO comparison contrasts auxiliary DINO features with using CLIP features alone.Source: Table IV · p. 12, Figure 9 · p. 12, Figure 10 · p. 12
  • The sampling-speed table reports faster sampling while maintaining superior generation performance.It gives per-instance sampling time on one V100 alongside latent size.Source: Table V · p. 12

APPENDIX

  • The paper notes that its high-quality texture and geometry generation could be misused to create deceptive 3D assets or falsified images and videos.The stated concern is potential exploitation to spread misinformation or tarnish reputations.Source: APPENDIX · p. 13

A. Training details

  • Training details specify U-Net for ShapeNet and FFHQ, DiT-L for Objaverse, and a lighter convolutional encoder; the authors note that compute limits motivated a smaller model.The Objaverse DiT-L has 24 layers, 16 heads, a 1024-dimensional hidden size, and 458M parameters.Source: A. Training details · p. 13

B. Data and Baseline Comparison

  • The evaluation uses ShapeNet and a high-quality Objaverse subset, with geometry measured using COV and MMD; FID/KID comparisons are standardized by re-rendering baseline samples.The paper uses artist-created datasets and describes baseline reproduction or checkpoint constraints for several comparisons.Source: B. Data and Baseline Comparison · pp. 13–14

C. More Qualitative 3D Generation Results

  • Supplementary qualitative results include two DiT-generated Objaverse samples per text prompt and additional uncurated ShapeNet, FFHQ, and category-free text-conditioned outputs.The ShapeNet and FFHQ samples are identified separately from the category-free text-conditioned results.Source: Figure 11 · p. 14, C. More Qualitative 3D Generation… · p. 14

D. More Monocular 3D Reconstruction Results

The appendix reports monocular reconstruction results and details limitations in the VAE and 3D diffusion stages. It also includes contributor biographies.

D. More Monocular 3D Reconstruction Results

  • The stage-1 VAE achieves comparable monocular reconstruction performance on ShapeNet-SRN chairs, with other baselines included mainly for reference.The comparison uses the 128 × 128 evaluation setting; the table caption notes that only Pix2NeRF shares the VAE's explicit generative latent-space setting.Source: D. More Monocular 3D Reconstruction… · p. 14, Table VII · p. 16
  • 26.14 average PSNR on the full Objaverse dataset accompanies high-quality, view-consistent novel-view reconstructions from one input view.Figure 15 shows the input, four reconstructed novel views, and corresponding depth maps, with artifacts labeled in red.Source: D. More Monocular 3D Reconstruction… · p. 15, Figure 15 · p. 18

E. VAE Limitations

  • For detail-rich challenging cases, monocular input can yield blurry and floating unseen-view artifacts; the authors hypothesize ambiguity and regression losses contribute and say multi-view encoding is necessary.Source: E. VAE Limitations · p. 16
  • Because the VAE requires Plücker camera conditions, its pretrained version cannot be directly applied to unposed datasets.Source: E. VAE Limitations · p. 16

F. 3D Diffusion Limitations

  • Training on object-centric data with simple captions leaves the diffusion model unable to generate composed 3D objects; a “Two chair” prompt can still yield one chair.Source: Figure 14 · p. 16, F. 3D Diffusion Limitations · p. 17
  • For Objaverse image-to-3D, the current model conditions on 77-token CLIP text embeddings, which capture high-level semantics better than low-level visual detail.The authors identify more accurate image-conditioned diffusion, such as ControlNet, as future work.Source: F. 3D Diffusion Limitations · p. 17
  • A high-quality UV texture map remains a requirement for closer integration with gaming and movie-industry workflows; the authors suggest disentangling geometry and texture and connecting them to UV space.Source: F. 3D Diffusion Limitations · p. 17

G. Biographies

  • The biographies describe contributors’ academic appointments and research spanning 3D representation learning, generative modeling, and image/video restoration.Source: G. Biographies · pp. 19–20

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