arXiv Machine Learning
RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives
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arXiv Machine Learning
Jun 30
RenderFormer++: Scalable and Physically Grounded Feed-Forward Neural Rendering
arXiv:2606. 30380v1 Announce Type: cross Abstract: We present RenderFormer++, a scalable and physically grounded feed-forward neural rendering framework for global illumination in mesh scenes.
arXiv Machine Learning
Aug 10
RenderFormer++: Scalable and Physics-Informed Feed-Forward Neural Rendering
arXiv:2606. 30380v2 Announce Type: replace-cross Abstract: We present RenderFormer++, a scalable and physics-informed feed-forward neural rendering framework for global illumination in mesh scenes.
arXiv Computer Vision
Oct 26
RADIANT: Object-Centric Feed-Forward Global Illumination
arXiv:2610.32075v1 Announce Type: new Abstract: Feed-forward neural renderers synthesize global illumination across novel 3D scenes without per-scene optimization or runtime ray tracing. Existing tra...
arXiv AI
Sep 10
RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting
RelightFormer is a feed‑forward generative Transformer that performs single‑ and multi‑view image relighting without explicit intrinsic property estimation. It incorporates a latent illumination module that injects target environment maps into spatial features via cross‑attention, and uses permutation‑invariant positional encodings to process unordered multi‑view inputs symmetrically. Trained on the large Laval Objaverse Dataset, the model achieves state‑of‑the‑art visual and photorealistic relighting quality, and demonstrates strong zero‑shot generalization across various relighting tasks.
arXiv Computer Vision
Oct 8
LightCrafter: PBR-Conditioned Video Diffusion Refinement for Controllable and Consistent Relighting
arXiv:2607.08016v3 Announce Type: replace Abstract: Video relighting requires balancing long-form temporal consistency with a physically grounded understanding of light transport, which depends on ac...
arXiv AI
Sep 21
Physically Based Rendering in the Latent Space
The paper introduces a method that applies physically based rendering (PBR) within the latent space of variational autoencoders used in image diffusion models. By modifying the rendering equation and using a differentiable renderer, the authors can generate latent maps that guide content creation with physically accurate lighting. The approach is trained on a single rendered image and then shown to generalize to changes in scene geometry, lighting, and camera viewpoint.