arXiv Computer Vision August 24, 2026 By Jiakun Li, Li Fang, Hao Zhu, Fei Hu, Long Ye, Yuan Zhang, Jinyao Yan

DiGS-Avatar: Single-Image Animatable 3D Human Reconstruction via UV-Space Diffusion

Read the original on arXiv Computer Vision →

The Flow has not summarised this story yet — read it at arXiv Computer Vision.

arXiv Machine Learning

Jul 2

Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures

arXiv:2605. 04035v3 Announce Type: replace-cross Abstract: We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups.

By Evangelos Ntavelis, Sean Wu, Mohamad Shahbazi, Fabio Maninchedda, Dmitry Kostiaev, Artem Sevastopolsky, Vittorio Megaro, Trevor Phillips, Alejandro Blumentals, Shridhar Ravikumar, Mehak Gupta, Reinhard Knothe, Jeronimo Bayer, Matthias Vestner, Simon Schaefer, Thomas Etterlin, Christian Zimmermann, Alexey Artemov, Mathias Deschler, Peter Kaufmann, Stefan Brugger, Sebastian Martin, Brian Amberg, Tom Runia

diffusion benchmarks

Hugging Face Trending Papers

Jun 24

PRISM: Feed-Forward Single-Image 3D Reconstruction via Geometric Warp-Residual Modeling

Reconstructing 3D scenes from a single image is a fundamental challenge in computer vision, with broad applications in virtual reality, robotics, and content creation. Recent methods achieve outstanding performance by leveraging camera-controlled video diffusion models, but rely on iterative diffusion sampling, which greatly limits their practical deployment.

diffusion computer-vision robotics fine-tuning efficiency benchmarks

arXiv AI

Jun 8

Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment

arXiv:2606. 07117v1 Announce Type: cross Abstract: This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations.

By Yibo Liu, Ziwei Zhang, Haozhou Pang, Menghao Li, Lanshan He, Gan Qi

llms diffusion fine-tuning safety

arXiv Computer Vision

2d ago

RecGen3D: Reconstruction-Guided 3D Generation in a Shared Canonical Space

RecGen3D is a framework that merges feed‑forward reconstruction and diffusion‑based generation to address the trade‑off between reconstruction fidelity and generative plausibility in sparse‑view 3D modeling. By aligning both models in a shared canonical space and using decoupled cooperative learning, the system stabilizes training and allows the reconstruction module to supply canonical geometric anchors while the diffusion generator refines and completes the structure. Experiments show that RecGen3D outperforms existing methods in producing complete and consistent 3D models from sparse observations.

By Zhisheng Huang, Jiahao Chen, Cheng Lin, Chenyu Hu, Hanzhuo Huang, Zhengming Yu, Mengfei Li, Yuheng Liu, Zekai Gu, Zibo Zhao, Yuan Liu, Xin Li, Wenping Wang

diffusion safety

arXiv AI

Jun 4

SAM 3D: 3Dfy Anything in Images

arXiv:2511. 16624v2 Announce Type: replace-cross Abstract: We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image.

By SAM 3D Team, Xingyu Chen, Fu-Jen Chu, Pierre Gleize, Kevin J Liang, Alexander Sax, Hao Tang, Weiyao Wang, Michelle Guo, Thibaut Hardin, Xiang Li, Aohan Lin, Jiawei Liu, Ziqi Ma, Anushka Sagar, Bowen Song, Xiaodong Wang, Jianing Yang, Bowen Zhang, Piotr Doll\'ar, Georgia Gkioxari, Matt Feiszli, Jitendra Malik

benchmarks safety

arXiv Computer Vision

1d ago

Photorealistic Novel View Synthesis of Human Faces using Next-Scale Transformers

arXiv:2608.23410v1 Announce Type: new Abstract: Photorealistic novel view synthesis of people remains challenging at high spatial resolutions and across multiple target cameras, where preserving iden...

By Federico Stella, Fei Jiang, Zhongshi Jiang, Zohar Barzelay, Emanuel Garbin, Amin Jourabloo, Liuhao Ge

llms diffusion