PaperlayerView exact revision on arXiv

Source-linked AI summary

GIRAFFE HD: A High-Resolution 3D-aware Generative Model

Yang Xue, Yuheng Li, Krishna Kumar Singh, Yong Jae Lee

arXiv:2203.14954v1cs.CVcs.AIcs.LG

TL;DR

GIRAFFE offers 3D control but is limited to low-resolution images, while high-resolution rendering with a style-based renderer can entangle foreground and background. GIRAFFE HD uses separate style-based foreground and background generation with compatibility constraints, and reports high-resolution generation with improved realism and disentanglement over GIRAFFE. Its current architecture cannot handle interlocking object relationships.

  • Problem

    GIRAFFE’s neural renderer limits resolution to 256^2, while computationally expensive 3D representations constrain high-resolution 3D-aware generation.

    Source: §1 · p. 1

  • Method

    GIRAFFE HD uses a style-based renderer, independently generates foreground and background, and imposes geometric and photometric compatibility constraints.

    Source: §1 · p. 2

  • Results

    Across multiple natural-image datasets, GIRAFFE HD demonstrates better foreground-background disentanglement and image realism than GIRAFFE in higher-resolution domains, with outputs up to 1024^2 versus GIRAFFE’s 256^2.

    Source: §1 · p. 2

  • Takeaways & Limitations

    GIRAFFE HD preserves GIRAFFE’s 3D controllable features while generating high-quality, high-resolution images.

    Source: §5 · p. 8

    The current architecture cannot handle interlocking object relationships, where objects appear both in front of and behind one another along different rays.

    Source: §5 · p. 8

Abstract

from arXiv · show

3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. In particular, the current state-of-the-art model GIRAFFE can control each object's rotation, translation, scale, and scene camera pose without corresponding supervision. However, GIRAFFE only operates well when the image resolution is low. We propose GIRAFFE HD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE's controllable features while generating high-quality, high-resolution images ($512^2$ resolution and above). The key idea is to leverage a style-based neural renderer, and to independently generate the foreground and background to force their disentanglement while imposing consistency constraints to stitch them together to composite a coherent final image. We demonstrate state-of-the-art 3D controllable high-resolution image generation on multiple natural image datasets.

1. Introduction

High-resolution style-based GANs lack explicit 3D control, while 3D-aware models have been limited by the computational cost of high-resolution rendering. GIRAFFE HD combines a style-based renderer with separate foreground and background generation and compatibility constraints to retain 3D control at higher resolutions.

Motivation

  • Style-based GANs produce photorealistic high-resolution images and offer some 2D control, but lack explicit 3D information for 3D-level control.NeRF-based GANs can provide 3D-level control, while the computational cost of 3D representations limits high-resolution rendering.Source: §1 · p. 1
  • GIRAFFE separates foreground and background as 3D objects and supports object and scene control, but its spatially limited neural renderer reaches only 256^2 resolution.The renderer is intentionally constrained to avoid entangling global scene properties and losing controllability.Source: §1 · p. 1

Motivation and design

  • A style-based renderer enables high-resolution output but gains control over color and some shape, entangling foreground and background features.The paper addresses this by generating the two independently and imposing geometric and photometric compatibility constraints.Source: §1 · p. 2

Contributions

  • GIRAFFE HD retains GIRAFFE’s 3D controllability while generating higher-quality images at up to 1024^2, compared with GIRAFFE’s 256^2.It is validated on five natural-image datasets and reports better foreground-background disentanglement and image realism than GIRAFFE in higher-resolution domains.Source: §1 · p. 2

2. Related Work

Related work spans implicit 3D scene representations, high-resolution style-based synthesis, and disentangled generation. GIRAFFE offers 3D control, while GIRAFFE HD combines independent foreground-background generation with compatibility constraints to address the quality and disentanglement trade-off.

3D-aware image synthesis

  • NeRF-based GANs model 3D scenes conditioned on noise or appearance and shape codes, enabling training on unstructured image collections rather than single scenes.GIRAFFE HD extends this line of work to higher-resolution domains while retaining 3D understanding capabilities.Source: §2 · p. 2

High-resolution image synthesis

  • Style-based GANs provide high-resolution outputs and some feature disentanglement; prior work also uses StyleGAN2 to upsample low-resolution feature maps.GIRAFFE HD leverages StyleGAN2 as its neural renderer for 512^2 resolution and higher.Source: §2 · p. 2

Disentanglement and controllability

  • Most disentangled-representation methods operate in 2D without considering 3D object or scene structure.Source: §2 · p. 2
  • GIRAFFE’s 3D scene representation disentangles object and scene factors, but replacing its renderer with a style-based one trades foreground-background disentanglement for high-resolution synthesis.Earlier two-stage generators compose independently generated foregrounds and backgrounds, but the passage attributes their image-quality shortfall presumably to insufficient information sharing and compatibility constraints.Source: §2 · p. 2
  • Geometric and photometric compatibility constraints in the two-stage approach lead to accurate foreground-background disentanglement in the high-resolution image domain.The described design shares 3D information and background-conditioned environment information between the branches.Source: §2 · p. 3

3. Approach

GIRAFFE HD builds on GIRAFFE with a StyleGAN2-based renderer and separate foreground and background streams. Compatibility mechanisms, 2D composition, and auxiliary training losses support coherent high-resolution images with foreground-background disentanglement.

3. Approach

  • The model separately generates foreground and background images, then combines them under compatibility constraints to form the final output.It replaces GIRAFFE’s low-capacity neural renderer with a StyleGAN2-based renderer.Source: §3 · p. 3

3.1. Background on GIRAFFE

  • GIRAFFE represents objects with separate 3D feature fields and affine transforms, then volume-renders their features into low-resolution maps for a neural renderer.The feature field maps encoded location and viewing direction, conditioned on a latent code, to density and appearance features; the object transform enables 3D object-level control.Source: §3.1 · pp. 3–4

3.2. Neural style rendering

  • A StyleGAN2-based renderer upsamples volume-rendered 16^2 feature maps to outputs of at least 512^2, but shifts fine shape and color control into its stages and entangles foreground with background.The method uses separate image-generation branches and injects shape codes into earlier stages and appearance codes into later stages to recover control.Source: §3.2 · p. 4

3.3. Enforcing foreground-background consistency

  • Position sharing aligns viewing perspective and, for objects resting on a ground plane, shares z-translation; environment sharing conditions foreground refinement on background appearance.For datasets without ground-resting foregrounds, the method synchronizes perspective without sharing z-translation; refinement adjusts shading and shine while preserving true appearance.Source: §3.3 · pp. 4–5

3.4. Compositing the final image

  • A generated foreground mask alpha-composites the foreground over the background; multiple foregrounds are composed recursively from farthest to nearest by depth.Source: §3.4 · p. 5

3.5. Training

  • Training combines GAN and R1 objectives with bounding-box containment, foreground coverage, and mask-binarization losses to support foreground-background separation.Containment discourages the foreground branch from generating background features, coverage discourages an empty foreground, and the auxiliary losses prevent either renderer from generating the entire image.Source: §3.5 · p. 5

4. Experiments

Experiments show that GIRAFFE HD improves image quality at higher resolutions while retaining GIRAFFE’s 3D controls. Ablations examine the roles of separate rendering, foreground-background consistency constraints, and auxiliary losses.

4. Experiments

  • Evaluation spans five natural-image datasets and compares GIRAFFE HD with GIRAFFE and other baselines using FID and foreground-background disentanglement measures.The experiments also assess 3D controllability and ablate model components and losses.Source: §4 · pp. 5–7

4.1. Image generation quality

  • GIRAFFE HD outperforms baselines by a large margin in image quality at 256² and at 512² on CompCar and 1024² on FFHQ.The comparisons cover FID at 256² and the higher-resolution settings.Source: Table 2 · p. 7, Table 1 · p. 7, §4.1 · p. 7

4.2. Controllable scene generation

  • GIRAFFE HD preserves GIRAFFE’s 3D controls, and foreground edits leave the background more consistent, reflected in lower MBS.The paper attributes the improved background consistency to explicit separation of foreground and background generation.Source: Figure 4 · p. 6, §4.2 · p. 7, Table 3 · p. 7

4.3. Ablation studies

  • A single style-based renderer breaks final-image foreground-background disentanglement: changing the car’s shape or color also changes the background.The figure illustrates entanglement in the single-renderer baseline.Source: §4.3 · pp. 7–8, Figure 6 · p. 7
  • Removing either position or environment sharing worsens FID and permits geometrically or photometrically incompatible foreground-background combinations.Table 4 reports FID for the full model and variants without these sharing choices.Source: Table 4 · p. 7, §4.3 · p. 8
  • Without bounding box containment loss the foreground branch generates the whole image; without foreground coverage loss the background branch does.These outcomes appear after 4,000 training iterations and motivate both losses for foreground-background disentanglement.Source: §4.3 · p. 8, Figure 7 · p. 8

5. Discussion and Conclusion

GIRAFFE HD extends GIRAFFE’s 3D controllability to high-resolution image generation. Its limitations include occasional 3D inconsistency, incompatible foreground-background combinations across categories, and inability to represent interlocking objects.

5. Discussion and Conclusion

  • GIRAFFE HD inherits GIRAFFE’s 3D controllable features while generating high-quality, high-resolution images.The paper identifies CompCar, FFHQ, AFHQ Cat, CelebA-HQ, and LSUN Church as evaluation datasets.Source: §5 · p. 8
  • When trained from scratch on CompCar, the model may fail to perform full 360 rotation, whereas initializing from a pretrained GIRAFFE 3D feature generator enables it.The from-scratch model can associate shape codes with front- or back-facing cars that each rotate only 180 degrees.Source: §5 · p. 8
  • A single model trained across multiple categories can produce high-quality images with incompatible foreground-background combinations.The paper cites cats, dogs, and wildlife as examples of categories.Source: §5 · p. 8
  • The architecture cannot handle interlocking objects, where an object appears both in front of and behind another along different rays.The proposed possible extension is object-mask rendering based on 3D occlusion reasoning.Source: §5 · p. 8

A. Full Loss Expression

The appendix presents the overall objective for a generator-discriminator pair, combining adversarial terms with dataset-specific loss components.

A. Full Loss Expression

  • The overall objective is formalized for a given generator-discriminator pair {G, D}.The displayed expression includes expectations over generated samples and data, plus L_bbox, L_cvg, and L_bin terms.Source: A. Full Loss Expression · p. 11
  • The objective’s generated-sample term uses sampled z_a^k, z_s^k, T^k, and ξ inputs.The provided expression passage includes these sampled variables in the generator-discriminator term.Source: A. Full Loss Expression · p. 11
  • The expression uses f(t) = −log(1+exp(−t)), λ = 10, and dataset-specific β1, β2, and β3 weights.The passage identifies pD as the data distribution and refers to the main paper for definitions of L_bbox, L_cvg, and L_bin.Source: A. Full Loss Expression · p. 11

B. Mutual Background Similarity (MBS) Details

Mutual background similarity (MBS) measures RGB changes in the shared background region when foreground parameters change while background parameters stay fixed. The appendix also notes that segmentation imprecision can contribute to measured differences.

B. Mutual Background Similarity (MBS) Details

  • MBS compares two generated images that share background parameters but use different foreground parameters, then counts RGB changes within their mutual background area.DeepLabV3 ResNet101 produces the semantic maps used to derive the background masks.Source: B. Mutual Background Similarity (MBS)… · p. 11
  • A pixel is counted as changed when one or more RGB channels changes beyond threshold η; images are normalized to [0,1] and η is set to 1/255.The changed-pixel count is taken within the mutual background area.Source: B. Mutual Background Similarity (MBS)… · p. 11
  • On FFHQ and CompCar, reported mutual-background differences mainly reflect segmentation imprecision rather than only image changes.The paper attributes FFHQ differences to difficulty segmenting thin floating hair and CompCar differences to omitted car shadows.Source: B. Mutual Background Similarity (MBS)… · p. 11

C. Dataset Details

The appendix reports dataset-dependent camera elevation and valid object-transformation parameters, reusing GIRAFFE settings for most datasets with stated exceptions.

C. Dataset Details

  • Table 5 reports dataset-dependent camera elevation angles and valid object-transformation parameters used across datasets.The passage identifies these as dataset parameters.Source: C. Dataset Details · p. 11
  • CompCar, FFHQ, LSUN Church, and CelebA-HQ use the same dataset parameters as GIRAFFE, except for CompCar’s vertical translation..Source: C. Dataset Details · p. 11
  • For AFHQ Cat, the authors use the dataset parameters that GIRAFFE uses for Cats because GIRAFFE was not evaluated on AFHQ Cat..Source: C. Dataset Details · p. 11

D. Additional Qualitative Results

Additional qualitative results show controllable scene synthesis across four datasets, with examples of background, appearance, shape, rotation, translation, and scale changes. The section also notes dataset-specific limits on how background and appearance controls affect foregrounds.

Additional qualitative results

  • CompCar, FFHQ, AFHQ Cat, and LSUN Church results are shown at 512^2, 1024^2, 256^2, and 256^2 resolution, respectively.GIRAFFE samples are included for direct comparison; CelebA-HQ results are omitted because they are very similar to FFHQ.Source: D. Additional Qualitative Results · p. 11
  • Examples vary backgrounds, appearance, shape, rotation, camera elevation, translation, and scale across the datasets.LSUN Church uses a fixed camera elevation during training, so its examples vary rotation only.Source: Figure 11 · p. 13, Figure 13 · p. 15, Figure 15 · p. 17
  • On LSUN Church, limited foreground appearance variation lets the refine renderer exert more control, so background changes can alter foreground appearance.The foreground appearance code also has relatively less effect on the object in this setting.Source: Figure 12 · p. 14, Figure 14 · p. 16
  • Changing camera elevation has little effect on AFHQ Cat results; the authors attribute this to the dataset’s small size.Source: Figure 18 · p. 20

Related papers

Loading 2203.14954v1…