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4Diffusion: Multi-view Video Diffusion Model for 4D Generation

Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu, Yunhong Wang, Yu Qiao

arXiv:2405.20674v2cs.CV

TL;DR

Existing diffusion-based 4D generation lacks multi-view spatial-temporal modeling, and combining multiple diffusion priors can produce temporal inconsistencies. 4Diffusion trains 4DM, a unified multi-view video diffusion model, and uses it with 4D-aware SDS and an anchor loss to optimize dynamic NeRF. The authors report state-of-the-art results for multi-view video generation and monocular-video 4D generation.

  • Problem

    Diffusion-based 4D methods lack multi-view spatial-temporal guidance, and integrating multiple diffusion priors can lead to inconsistent temporal appearance and flickers.

    Source: §1 · p. 2

  • Method

    4Diffusion trains 4DM by adding a learnable motion module to a 3D-aware diffusion model, then uses 4D-aware SDS and an anchor loss to optimize dynamic NeRF.

    Source: §1 · pp. 2–3

  • Results

    The authors report state-of-the-art performance on multi-view video generation and 4D generation from monocular videos.

    Source: §1 · p. 3

  • Takeaways & Limitations

    The resulting dynamic NeRF content is described as spatial-temporally consistent and coherent with the input monocular video.

    Source: §5 · p. 10

    The model is constrained by the base model's capability and the scale of high-quality training data, while volumetric rendering slows training.

    Source: §A.3 · p. 17

Abstract

from arXiv · show

Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. However, these methods lack multi-view spatial-temporal modeling and encounter challenges in integrating diverse prior knowledge from multiple diffusion models, resulting in inconsistent temporal appearance and flickers. In this paper, we propose a novel 4D generation pipeline, namely 4Diffusion, aimed at generating spatial-temporally consistent 4D content from a monocular video. We first design a unified diffusion model tailored for multi-view video generation by incorporating a learnable motion module into a frozen 3D-aware diffusion model to capture multi-view spatial-temporal correlations. After training on a curated dataset, our diffusion model acquires reasonable temporal consistency and inherently preserves the generalizability and spatial consistency of the 3D-aware diffusion model. Subsequently, we propose 4D-aware Score Distillation Sampling loss, which is based on our multi-view video diffusion model, to optimize 4D representation parameterized by dynamic NeRF. This aims to eliminate discrepancies arising from multiple diffusion models, allowing for generating spatial-temporally consistent 4D content. Moreover, we devise an anchor loss to enhance the appearance details and facilitate the learning of dynamic NeRF. Extensive qualitative and quantitative experiments demonstrate that our method achieves superior performance compared to previous methods.

1 Introduction

Prior diffusion-guided methods focused on static 3D, while 4D generation requires vivid motion and spatial-temporal consistency. 4Diffusion addresses this with a unified multi-view video diffusion model and losses for optimizing dynamic NeRF from monocular video.

1 Introduction

  • Earlier diffusion-guided methods created static 3D shapes, whereas 4D generation must combine vivid motion with high-quality spatial-temporal consistency.Prior 4D approaches used multiple diffusion models, but lacked accurate multi-view spatial-temporal guidance and could produce inconsistent temporal appearance and flicker.Source: §1 · p. 2
  • 4DM adds a learnable motion module to a pre-trained 3D-aware diffusion model to capture multi-view spatial-temporal correlations.It trains on only hundreds of curated multi-view videos while keeping the 3D-aware model's parameters unchanged to preserve generalizability and spatial consistency.Source: §1 · pp. 2–3
  • 4Diffusion uses 4D-aware SDS and an anchor loss based on 4DM to optimize dynamic NeRF, stabilizing training and supporting high-quality 4D content.The anchor loss enhances appearance details and facilitates dynamic NeRF learning.Source: §1 · p. 3
  • Experiments report state-of-the-art performance in multi-view video generation and 4D generation from monocular videos.The resulting 4D content is described as spatial-temporally consistent and coherent with the input video.Source: §1 · p. 3

2 Related Work

Related work spans static 3D generation, video and 3D-aware diffusion, and 4D generation from several kinds of prompts. The paper distinguishes 4Diffusion by modeling multi-view spatial-temporal correlations in a unified diffusion model.

2 Related Work

  • Diffusion-prior methods use SDS to optimize 3D representations, while feed-forward methods can reconstruct shapes quickly but require extensive 3D training data.The section groups 3D generation into generative, feed-forward, and diffusion-prior-based methods, contrasting these static-3D approaches with the paper's 4D focus.Source: §2 · p. 3
  • Video diffusion models add temporal modeling, while 3D-aware diffusion models model joint multi-view images; the cited approaches do not generate multi-view consistent videos.AnimateDiff [16] inserts a motion module into a frozen text-to-image model, whereas 3D-aware approaches [31] [44] [59] [32] [52] use 3D-aware attention.Source: §2 · p. 3
  • Prior 4D methods combine 3D-aware, image, and video diffusion guidance, but lack multi-view spatial-temporal consistency and face challenges integrating those priors.MAV3D [47] uses a static-to-dynamic, two-stage approach; Dream-in-4D and 4D-fy [4] combine hybrid diffusion guidance.Source: §2 · pp. 3–4
  • Among monocular-video approaches, Consistent4D lacks cross-frame temporal modeling, while Gaussian-splatting methods [42] [62] [57] may yield blurred appearance and inaccurate geometry.STAG4D [63] uses a training-free strategy to generate sparse anchor multi-view videos; 4Diffusion instead proposes a multi-view video diffusion model for consistency guidance.Source: §2 · p. 4

3 Method

4Diffusion starts from ImageDream’s multi-view spatial prior, adds temporal modeling in 4DM, and uses 4DM guidance and an anchor loss to optimize dynamic NeRF from monocular video. The pipeline targets arbitrary-view, temporally consistent 4D content.

3 Method

  • The method’s input is a monocular video, and its stages produce multi-view videos before distilling their prior into a dynamic NeRF representation.The overall objective is spatial-temporally consistent 4D content that can render from novel viewpoints over time.Source: §3 · p. 4, §3.2 · p. 5, §3.3 · p. 6

3.1 Preliminary: 3D-aware Diffusion Models

  • ImageDream initializes 4DM because its multi-view spatial prior can be extended to arbitrary-view video generation, unlike sparse-view methods that require additional reconstruction.ImageDream synthesizes four coherent views from an image and optional text using an adapter and 3D self-attention.Source: §3.1 · pp. 4–5

3.2 4DM: Multi-view Video Diffusion Model

  • 4DM adds zero-initialized motion modules to frozen ImageDream blocks, combining temporal self-attention with spatial and cross-attention processing.The motion modules address ImageDream’s lack of temporal modeling while retaining its spatial model parameters unchanged.Source: §3.2 · p. 5
  • Training 4DM on 926 curated animated Objaverse shapes yields reasonable temporal dynamics while preserving ImageDream’s generalization and spatial consistency.The authors render multi-view videos from the curated subset and tune the motion module while keeping ImageDream frozen.Source: §3.2 · p. 5

3.3 4D Generation

  • The pipeline uses 4D-aware SDS and an anchor loss to optimize dynamic NeRF, aiming to support rendering from novel viewpoints over time while improving appearance detail.4D-aware SDS uses guidance from 4DM; the anchor video is selected from four orthogonal videos conditioned on the input, with LPIPS and SSIM used as image-level perceptual losses.Source: §3.3 · pp. 6–7

4 Experiments

The experiments compare 4Diffusion with prior 4D-generation methods, evaluate 4DM for multi-view video generation, and test the effects of dataset curation and the proposed losses. The reported results favor 4Diffusion across 4D-generation metrics and show that 4DM produces spatial-temporally consistent multi-view videos.

4 Experiments

  • 4DM is trained on 256×256 multi-view videos for 30,000 steps, while its 4D-generation experiments optimize dynamic NeRF end to end, avoiding the multiple stages used in previous works.Source: §4 · p. 7

4.1 Comparisons on 4D Generation

  • Qualitatively, 4Diffusion produces high-quality 4D content, while 4D-fy shows inconsistent temporal appearance, Consistent4D flickers, and DreamGaussian4D has blurred appearance and inaccurate geometry.The comparison uses monocular video inputs and includes 4D-fy [4], Consistent4D, and DreamGaussian4D [42].Source: §4.1 · pp. 7–8, Figure 7 · p. 8
  • Using ground-truth videos for novel viewpoints, 4Diffusion outperforms other methods on all metrics: FVD for video quality, LPIPS and PSNR for spatial consistency, and CLIP-C for temporal consistency.Source: §4.1 · p. 9

4.2 Multi-view Video Generation

  • 4DM captures temporal information and generates spatially consistent multi-view videos, including beyond the training distribution, while ImageDream can show inconsistent appearances or viewpoint-incoherent videos.The comparison illustrates synthesized videos from 4DM and ImageDream [52].Source: §4.2 · p. 9, Figure 8 · p. 9
  • 4DM performs comparably to ImageDream on CLIP-I and better on CLIP-C, FVD, LPIPS, and PSNR for multi-view video generation.These metrics assess temporal and spatial fidelity as well as image quality.Source: §4.2 · pp. 9–10, Table 2 · p. 10

4.3 Ablation study and analysis

  • Using the entire animated Objaverse dataset compromises ImageDream’s image quality and makes spatial-temporal correlations harder to capture, supporting the value of high-quality dataset curation.The comparison fine-tunes 4DM on the full dataset rather than the selected high-quality videos.Source: §4.3 · p. 10
  • Replacing 4DM with ImageDream and 4D-aware SDS produces inconsistent temporal textures, while omitting anchor loss makes detailed appearance features harder to capture.The examples include squirrel-leg textures and eyes; Figure 9 presents the ablations.Source: §4.3 · p. 10, Figure 9 · p. 10

5 Conclusion

The conclusion presents 4Diffusion as a monocular-video-to-4D pipeline. It combines 4DM with 4D-aware SDS and anchor loss to optimize dynamic NeRF for spatial-temporally consistent content coherent with the input video.

5 Conclusion

  • 4Diffusion uses 4DM to provide multi-view spatial-temporal guidance and combines 4D-aware SDS with anchor loss to optimize hash-encoded dynamic NeRF.The conclusion says 4DM captures spatial-temporal correlations while preserving characteristics of its 3D-aware diffusion model after training on a small curated dataset.Source: §5 · p. 10

Acknowlegements

The work acknowledges support from Chinese national and Shanghai municipal research programs.

Acknowlegements

  • The work was supported by the National Key R&D Program of China, the National Natural Science Foundation of China, and the Science and Technology Commission of Shanghai Municipality.The acknowledgement lists grant numbers 2022ZD0160102, 62102150, and 23QD1400800, respectively.Source: Acknowlegements · p. 11

A.1 More Implementation Details

The supplementary implementation details describe dataset construction, model training, dynamic NeRF optimization, and rendering. They also identify the hash-encoding parameter table for spatial and spatiotemporal representations.

A.1 More Implementation Details

  • Table 3 lists hash-encoding parameters for Pxyz and Pxyzt.The supplied passage identifies the table’s scope but not its parameter values.Source: Table 3 · p. 15
  • The Objaverse curation retains 926 animated 3D models and renders 32 videos per model across uniformly spaced azimuth angles.Models with static objects, out-of-scene movement, rotated cameras, or meaningless content are filtered out.Source: §A.1 · p. 15, Figure 10 · p. 15
  • Dynamic NeRF optimization runs for 35,000 iterations, while volume rendering uses a shared occupancy grid based on maximum scene opacity across frames.The implementation uses AdamW and NerfAcc; rendering backgrounds are white and camera distance is fixed at 1.1.Source: §A.1 · pp. 15–16

A.2 More Results

Comparisons show 4Diffusion avoids the multi-face problem reported for Consistent4D and DreamGaussian4D. The paper also presents text-to-4D results and additional 4D-generation examples.

A.2 More Results

  • Figure 11 presents additional 4D-generation results, with supplementary videos recommended for a more intuitive view.The passage does not specify further outcomes for these examples.Source: §A.2 · p. 16
  • 4Diffusion generates spatial-temporally consistent content, while Consistent4D and DreamGaussian4D encounter the multi-face problem; comparison cases come from Objaverse and were excluded from 4DM training.The comparison is shown in Figure 12.Source: §A.2 · p. 17, Figure 12 · p. 17
  • Figure 13 illustrates high-quality 4D content generated from text prompts through SDXL images, SVD V1.1 monocular videos, and the 4Diffusion pipeline.The process first generates images from text with SDXL, then monocular videos with SVD V1.1, before generating 4D content.Source: §A.2 · p. 17

A.3 Limitations

The authors identify limits in both the multi-view video diffusion model and the 4D-generation pipeline’s training speed.

A.3 Limitations

  • 4DM is constrained by its base model’s capability and the scale of its high-quality training data.The authors suggest improving the base model and scaling the dataset as ways to improve it.Source: §A.3 · p. 17
  • Heavy reliance on volumetric rendering slows training, with advances in 3D and GS proposed as potential remedies.Source: §A.3 · p. 17

A.4 Broader Impacts

The authors say the work may reduce manual effort for artists and novices, while noting potential misuse and influence on human perceptions.

A.4 Broader Impacts

  • The authors frame 4D generation as a way to reduce artists’ and novices’ extensive manual effort.Source: §A.4 · p. 17
  • Although not designed for human generation, the method may be extended and misused in ways that influence human perceptions.Source: §A.4 · p. 17

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