Source-linked AI summary
Deferred Neural Rendering: Image Synthesis using Neural Textures
Justus Thies, Michael Zollhöfer, Matthias Nießner
TL;DR
The paper tackles photo-realistic image synthesis from imperfect reconstructed 3D content, which conventional graphics pipelines struggle to render faithfully. It introduces end-to-end learned neural textures and a deferred renderer integrated with the graphics pipeline, demonstrating applications across novel views, scene editing, and facial animation. The approach retains 3D-based control and temporal coherence while operating at real-time rates in the reported applications.
Problem
Conventional graphics pipelines require well-defined, high-quality 3D content, whereas reconstructed geometry may be noisy, oversmoothed, or incomplete.
Method
The method jointly learns high-dimensional neural textures on 3D mesh proxies and a deferred neural renderer within the traditional graphics pipeline.
Results
The paper demonstrates photo-realistic neural rendering from imperfect commodity 3D reconstructions at real-time rates across novel-view synthesis, scene editing, and facial animation.
Takeaways & Limitations
The 3D-embedded representation provides explicit manipulation control and temporally coherent generated video across static and dynamic scene applications.
Takeaways & Limitations
The method requires reconstructed geometry and object-specific neural-texture training, while real-data generalization is limited by a dataset containing fewer than 10 objects.
Abstract
from arXiv · showhide
The modern computer graphics pipeline can synthesize images at remarkable visual quality; however, it requires well-defined, high-quality 3D content as input. In this work, we explore the use of imperfect 3D content, for instance, obtained from photo-metric reconstructions with noisy and incomplete surface geometry, while still aiming to produce photo-realistic (re-)renderings. To address this challenging problem, we introduce Deferred Neural Rendering, a new paradigm for image synthesis that combines the traditional graphics pipeline with learnable components. Specifically, we propose Neural Textures, which are learned feature maps that are trained as part of the scene capture process. Similar to traditional textures, neural textures are stored as maps on top of 3D mesh proxies; however, the high-dimensional feature maps contain significantly more information, which can be interpreted by our new deferred neural rendering pipeline. Both neural textures and deferred neural renderer are trained end-to-end, enabling us to synthesize photo-realistic images even when the original 3D content was imperfect. In contrast to traditional, black-box 2D generative neural networks, our 3D representation gives us explicit control over the generated output, and allows for a wide range of application domains. For instance, we can synthesize temporally-consistent video re-renderings of recorded 3D scenes as our representation is inherently embedded in 3D space. This way, neural textures can be utilized to coherently re-render or manipulate existing video content in both static and dynamic environments at real-time rates. We show the effectiveness of our approach in several experiments on novel view synthesis, scene editing, and facial reenactment, and compare to state-of-the-art approaches that leverage the standard graphics pipeline as well as conventional generative neural networks.
1 INTRODUCTION
The paper addresses photo-realistic rendering from imperfect reconstructed 3D content by combining traditional graphics with learned components. Its 3D-embedded representation supports controllable, temporally coherent synthesis and applications including novel views, editing, and animation.
- Existing graphics pipelines require precise geometry, materials, and illumination, but reconstructed 3D content is often noisy, oversmoothed, or incomplete.
- Deferred Neural Rendering changes the rendering paradigm to accommodate reconstruction artifacts rather than repairing the captured 3D content.
- Neural Textures are high-dimensional learned feature maps stored on 3D meshes and optimized during scene capture instead of containing only RGB values.
- The paper targets photo-realistic synthesis from imperfect commodity reconstructions at real-time rates, including novel views, dynamic-object editing, and neural rendering.
- Embedding the representation in 3D provides temporally coherent video output and active control over manipulations compared with black-box 2D generative networks.
- The approach combines traditional graphics-based synthesis with learnable components in an end-to-end deferred neural rendering pipeline.
2 RELATED WORK
Related work spans reconstructed-scene rendering, image-based rendering, light fields, and neural image synthesis. Deferred Neural Rendering instead learns high-dimensional appearance features attached to a 3D proxy and interpreted by a neural renderer.
- Novel-view synthesis commonly begins with a digital representation of a real-world scene, but reconstruction imperfections hinder photo-realistic re-rendering.
- Image-based rendering uses coarse geometry to select and blend suitable source views, reducing reliance on detailed surface geometry.
- The paper's pipeline combines standard graphics rendering with learned neural textures and a deferred neural renderer for imperfect 3D surfaces.
- Surface light fields store direction-dependent radiance samples, whereas neural textures learn optimal features end-to-end for neural interpretation.
- Neural networks and conditional GANs synthesize 2D imagery, while related view-synthesis methods use posed images, learned warping, layered representations, or geometric constraints.
- The reenactment pipeline transfers a source actor's expression to the target through an altered UV map, neural texture sampling, and neural rendering with background input.
3 OVERVIEW
Neural Textures integrate learned appearance features into the standard graphics pipeline for novel-view synthesis and dynamic-scene editing. The system samples texture features into image space and uses a deferred renderer to produce imagery.
- Neural Textures are learned jointly with a Deferred Neural Renderer to enable photo-realistic synthesis from imperfect commodity 3D reconstructions.
- The standard graphics pipeline samples neural textures into a target-image-space feature map, which the deferred renderer converts into output imagery.
- Neural Textures can texture scanned meshes and learn photo-realistic novel-view synthesis when ground-truth color images are available.
- For dynamic scenes, the representation supports face reenactment by re-rendering modified UV maps with person-specific neural textures and renderers.
- The paper presents the pipeline stages and then evaluates applications including novel-view synthesis, scene editing, and facial animation editing.
4 DEFERRED NEURAL RENDERING
Deferred Neural Rendering combines neural textures with a traditional graphics pipeline, optimizing both textures and renderer end-to-end for image synthesis from imperfect 3D proxies.
- The approach jointly optimizes a neural texture T and deferred neural renderer R over posed training images using a photometric re-rendering loss.The training corpus contains images I_k with corresponding intrinsic and extrinsic camera parameters p_k.
- Neural textures extend conventional texture maps with learned high-dimensional feature maps rather than hand-crafted low-dimensional appearance values.The feature maps are learned from a ground-truth image corpus and interpreted by the deferred neural rendering pipeline.
- Neural Texture Hierarchies use K levels to represent low-frequency information coarsely and high-frequency detail finely, combining bilinearly sampled per-level features.The hierarchy addresses simultaneous texture minification and magnification across complex scenes and viewpoints.
- Neural textures support differentiable bilinear sampling, allowing end-to-end training with the Deferred Neural Renderer while matching graphics-pipeline operations at test time.The renderer forms a photo-realistic image from a screen-space feature map produced by rasterizing the textured geometric proxy.
- The method outperforms Pix2Pix for novel-view synthesis, producing less blurry views that are close to ground truth.The comparison is reported in Figure 4; no numeric metric is given in the supplied passages.
- Experiments use synthetic sequences with 1000 random training views and 1000 smooth-trajectory test views, while facial reenactment uses 650 to 2400 training images.The reported facial-reenactment training counts are 650 images for Macron and 2400 each for Obama and Sequence 17.
5 RESULTS
The experiments evaluate Deferred Neural Rendering for novel-view synthesis, scene editing, and facial reenactment, including comparisons, ablations, and real-time performance.
- Novel View Point Synthesis: The method re-renders objects photo-realistically and produces sharper, more temporally coherent results than Pix2Pix and more temporally stable results than IGNOR.It stores one neural texture instead of multiple original images for novel-view synthesis.
- Novel View Point Synthesis: 1686 views are used for Debevec et al.'s baseline, whereas Hedman et al. use 99 reconstruction frames; the proposed method needs neither training images nor per-frame depth at test time.The proposed representation consists of a 512 × 512 × 16 texture and a 16-million-parameter rendering network, but requires sequence-specific training.
- Novel View Point Synthesis: MSE reaches 0.418 at 256 × 256 for a single texture and 0.38 at 2048 × 2048 for a hierarchical texture.For the non-hierarchical texture, error increases to 8.46 at 4096 × 4096; the evaluation uses 1000 frames and color values in [0, 255].
- Novel View Point Synthesis: A very coarse proxy mesh still produces a reasonable output image, showing tolerance to reduced geometry resolution.The proxy geometry is progressively reduced using quadric edge collapse.
- Novel View Point Synthesis: View extrapolation produces artifacts, but reasonable views remain possible in regions represented in the training video.The paper reports extrapolation for both rotational and positional changes.
- Scene Editing and Animation Synthesis: Neural textures support moving, removing, or duplicating scene objects while generating temporally stable, high-quality results compared with standard image-to-image translation.The method is also applied to dynamic-object editing and facial reenactment.
- Scene Editing and Animation Synthesis: Facial reenactment uses one target-specific neural texture and renderer, with rendering taking approximately 4 ms versus 10 ms for Face2Face.The system captures target-actor idiosyncrasies and can reenact expressions from any new source actor.
- Limitations: The approach raises ethical concerns because realistic image synthesis can facilitate manipulation or fabricated imagery.The concern is stated in the context of tensions between authenticity and increasingly realistic generated media.
6 CONCLUSION
The paper demonstrates neural textures across novel view synthesis, scene editing, and facial animation editing, including photo-realistic multi-object edits and facial reenactment comparisons. It concludes that learned rendering components can help handle imperfect real-world 3D data while enabling further extensions.
- Neural textures support novel view synthesis, scene editing, and facial animation editing across a range of applications.
- Multi-object scene editing, including removal and cloning, produces photo-realistic results.
- Rendering quality degrades gracefully with coarse geometry, while each new object requires its own neural-texture training.
- The approach assumes static illumination and therefore cannot relight scenes, although extensions to other representations and rendering components are proposed.
- The authors frame neural textures as a first step toward learned graphics-pipeline elements for handling imperfect data captured from the real world.
- Compared with DeepVideoPortraits and Face2Face, the approach better captures target-actor idiosyncrasies with sharper, more varied mouth interiors and fewer stretching artifacts.
A NETWORK ARCHITECTURE
The rendering network uses a U-Net encoder-decoder with skip connections to transform rendered neural textures into final output images. The architecture processes an image containing 16 features per pixel.
- The rendering network is a five-layer U-Net encoder-decoder with skip connections that generates the final output image.
- The network takes rendered neural textures represented as an image with 16 features per pixel as input.