activity
20232026
most citedEra3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention

5 citations · 9 across the 13 of their papers we have counts for

collaborators
Showing 2026 · cs.CVShow all

6 papers · 2 filters

cs.CV2026

RIGen: Beyond Action Following with Reciprocal Interaction in Generative Gameplay

Mengfei Li, Yi Zhang, Xingtong Ge +8

Generative gameplay requires more than immediate responses to player actions.Actions should have persistent consequences, while the generated world should act according to its own…

cs.CV2026

PURE: Refining Feed-forward 3D Reconstruction with Unlabeled Post-training

Yaolong Zhu, Peng Li, Jiahao Lu +8

Feed-forward 3D reconstruction models have achieved remarkable progress through large-scale training and strong learned priors. However, they remain fragile when applied to dynamic…

cs.CV2026

Enhancing Autoregressive Video Generation via Representation Adversarial Distillation

Fangyu Lin, Xingtong Ge, Lunjie Zhu +8

Few-step autoregressive video generation enables efficient streaming synthesis, but errors introduced in early temporal blocks are reused as context and can propagate through subse…

cs.CV2026

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

Zhisheng Huang, Jiahao Chen, Cheng Lin +10

Sparse-view 3D modeling represents a fundamental tension between reconstruction fidelity and generative plausibility. While feed-forward reconstruction excels in efficiency and inp…

cs.CV2026

MV2UV: Generating High-quality UV Texture Maps with Multiview Prompts

Zheng Zhang, Qinchuan Zhang, Yuteng Ye +5

Generating high-quality textures for 3D assets is a challenging task. Existing multiview texture generation methods suffer from the multiview inconsistency and missing textures on…

cs.CV2026

UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass

Mengfei Li, Peng Li, Zheng Zhang +9

We present UniSH, a unified, feed-forward framework for joint metric-scale 3D scene and human reconstruction. A key challenge in this domain is the scarcity of large-scale, annotat…