Hugging Face Trending Papers
RealMe: Immersive Volumetric Video from Minimal Captures
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arXiv Computer Vision
3d ago
RealMe: Immersive Volumetric Video from Minimal Captures
arXiv:2610.30023v1 Announce Type: new Abstract: This paper addresses the problem of creating immersive volumetric videos from sparse and low-resolution capture resources. Conventional dense multi-vie...
Hugging Face Trending Papers
Aug 11
Beyond Pixels: From Video Priors to 4D Worlds
4D generation synthesizes dynamic 3D scenes from conditions such as text or images. Existing methods either reconstruct generated RGB videos with a separate 4D model or adapt a particular video generator to predict geometry directly.
arXiv AI
Sep 2
DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models
arXiv:2604.06161v3 Announce Type: replace-cross Abstract: Most digital videos are stored in 8-bit low dynamic range (LDR) formats, where much of the original high dynamic range (HDR) scene radiance i...
arXiv Computer Vision
Oct 5
UniDynamics: Event-RGB Fusion for Unified Future 4D Dynamic Scene Generation
UniDynamics is a diffusion-based framework that generates future 4D dynamic scenes—comprising RGB, depth, and optical flow—from a single event-RGB pair, without needing long histories or control priors. It introduces an Event Latent Enhancement module to align event data into conditioning features and a Perceptual Dynamics Space within a multi-scale U‑Net to decouple and adaptively interact depth and flow, enforcing geometric and motion constraints for coherent predictions. Experiments on VKitti2 and DSEC show state‑of‑the‑art performance, producing high‑quality, temporally coherent, and 4D‑consistent predictions even under high‑speed motion blur.
arXiv AI
Jul 7
InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem
arXiv:2512. 05672v2 Announce Type: replace-cross Abstract: Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs).
Hugging Face Trending Papers
Aug 9
MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling
Video super-resolution (VSR) aims to recover high-fidelity high-resolution videos from low-resolution inputs and is central to applications ranging from mobile capture to streaming and archival restoration. Existing approaches trade off among local-detail fidelity, long-range spatio-temporal modeling, perceptual realism, and efficiency: convolutional alignment techniques preserve local structure but suffer when motion is large or degradations are complex; transformer-based methods capture long-range dependencies yet require architectural or algorithmic adaptations to remain computationally feasible; and recent latent or diffusion-based generators synthesize rich texture but require specialized temporal constraints to maintain coherence.