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: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: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...
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: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...
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.
Large view synthesis models (LVSMs) reconstruct scenes from sparse views and render novel views quickly, but they incur unnecessary storage and rendering costs due to token redundancy. We present GLoD...
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.
NeuralSRender is a sim‑to‑real video translation framework that improves the photorealism of synthetic driving videos while preserving geometric consistency. It builds on a pretrained video diffusion transformer and uses a lightweight projector to inject G‑buffer geometric and material priors, with LoRA for efficient adaptation. The two‑stage training first learns G‑buffer conditioning from synthetic data, then adapts to real‑world appearance, adding only 1.45 % parameter overhead and requiring 24 GB GPU memory. The authors also release GenReal, a large‑scale dataset of synthetic and real driving videos with G‑buffers, and show that training perception models on NeuralSRender‑translated videos boosts detection AP₅₀ on nuImages from 35.1 to 40.6 and segmentation mIoU on Cityscapes from 32.4 to 40.8.
arXiv:2608.23549v1 Announce Type: new Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
DAGS introduces a lightweight, attention‑free conditioning scheme that disentangles appearance and geometry for a frozen image diffusion transformer (DiT), enabling high‑fidelity, temporally stable renders with independent control. Two small convolutional encoders generate per‑frame conditioning features, which are injected as learned residuals into the image tokens, avoiding the quadratic cost of attention. Coupled with a recurrent lighting stabilizer and a training‑free temporal guidance term, DAGS transforms a per‑frame image model into a streaming renderer that outperforms real‑time denoisers and diffusion renderers in PSNR and temporal stability while requiring far less compute than path tracing.
arXiv:2602.18741v3 Announce Type: replace-cross Abstract: Spectral rendering reproduces the wavelength-dependent appearance that RGB rendering cannot: metamerism, colour shifts under spectrally rich...