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.
By Huangsheng Du, Haoran Zhu, Youcheng Cai, Jingyang Meng, Ligang Liu
arXiv:2609.05738v1 Announce Type: cross
Abstract: We present 'RenderFormer-V2', a unified learned transformer-based neural rendering model, complementary to modern physics-based rendering systems, th...
By Chong Zeng, Yue Dong, Pieter Peers, Lvmin Zhang, Maneesh Agrawala
Recent advances in neural scene representations enable photorealistic novel-view synthesis, yet most methods remain tightly coupled to a single rendering paradigm, limiting their versatility and integration with conventional graphics workflows. We introduce Floating Radiance Networks (FlaRe), a neural scene representation combining explicit ray-traceable geometry with continuous neural radiance functions.
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.
By Hejun Wang, Jinxi Li, Junwei Jiang, Shiwei Mao, Hu Cheng, Shouwang Huang, Bo Yang
arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.
By Zhengqin Li, Cheng Zhang, Jakob Engel, Zhao Dong
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.
By Vuk Radovanovic, Vishesh Gupta, Adrien Gruson, Binh-Son Hua