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20222026
most citedStructured 3D Latents for Scalable and Versatile 3D Generation

8 citations · 16 across the 9 of their papers we have counts for

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9 papers · 1 filter

cs.CV2026

APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation

Sirui Zhang, Fangyun Wei, Dong Chen +5

Post-training is crucial for improving text-to-image models, yet existing pipelines typically rely on manually curated training data and fixed training distributions. We introduce…

cs.CV2026

LensErase: A Foundation Model for Erase Anything

Yunuo Chen, Xiuyu Wu, Qinhong Yang +11

We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. LensErase supports diverse ob…

cs.CV2026

Energy-Guided Flow Matching

Haoyang Tong, Yu He, Fang Li +6

Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flo…

cs.CV2026

HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion

Yu He, Lichen Ma, Zipeng Guo +5

Pixel-space diffusion models bypass the reconstruction bottleneck of Variational Autoencoders (VAEs) but face a fundamental "granularity dilemma": capturing global semantics favors…

cs.CV2026

LiWi: Layering in the Wild

Yu He, Fang Li, Haoyang Tong +7

Recent advances in generative models have empowered impressive layered image generation, yet their success is largely confined to graphic design domains. The layering of in-the-wil…

cs.CV2026

InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation

Yang Yue, Fangyun Wei, Tianyu He +10

Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on d…

cs.CV2025

Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis

Bowen Zhang, Sicheng Xu, Chuxin Wang +4

In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extrem…

cs.CV2024★ 8 cited

Structured 3D Latents for Scalable and Versatile 3D Generation

Jianfeng Xiang, Zelong Lv, Sicheng Xu +6

We introduce a novel 3D generation method for versatile and high-quality 3D asset creation. The cornerstone is a unified Structured LATent (SLAT) representation which allows decodi…

cs.CV2022★ 2 cited

A Structure-Guided Diffusion Model for Large-Hole Image Completion

Daichi Horita, Jiaolong Yang, Dong Chen +3

Image completion techniques have made significant progress in filling missing regions (i.e., holes) in images. However, large-hole completion remains challenging due to limited str…