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20242026
most citedInFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior

4 citations · 7 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2026

Thinking With Tools, Not With Pixels: Tool Calls as Text Scaffolds for Visual Reasoning

Jiahao Shao, Yuanbo Yang, Yiyi Liao +3

Tool-augmented vision-language models increasingly "think with images": they call crop, zoom, or code tools and reason over the returned pixels. However, recent work using blind te…

cs.RO2026

AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations

Ziyang Zhang, Boyang Zhou, Zesong Yang +8

Real-time navigation in cluttered and dynamic environments requires collision-free and dynamically feasible motion under limited perception. However, feasible navigation behaviors…

cs.CV2026

GARDEN: Gravity-Aligned Reconstruction of Disentangled ENvironments from RGB images

Jiahao Sun, Dingkun Wei, Zehong Shen +3

Converting multi-view RGB observations into simulation-ready 3D environments remains challenging because current reconstruction pipelines produce monolithic scene representations w…

cs.CV2026

AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation

Haobo Li, Yanhong Zeng, Yunhong Lu +6

We present AAD-1, an Asymmetric Adversarial Distillation framework for One-step autoregressive image-to-video generation. State-of-the-art methods adopt adversarial distillation bu…

cs.CV2026

ScenDi: 3D-to-2D Scene Diffusion Cascades for Urban Generation

Hanlei Guo, Jiahao Shao, Xinya Chen +4

Recent advancements in 3D object generation using diffusion models have achieved remarkable success, but generating realistic 3D urban scenes remains challenging. Existing methods…

cs.CV2025

Orientation Matters: Making 3D Generative Models Orientation-Aligned

Yichong Lu, Yuzhuo Tian, Zijin Jiang +7

Humans intuitively perceive object shape and orientation from a single image, guided by strong priors about canonical poses. However, existing 3D generative models often produce mi…

cs.CV2025

FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views

Shangzhan Zhang, Jianyuan Wang, Yinghao Xu +5

We present FLARE, a feed-forward model designed to infer high-quality camera poses and 3D geometry from uncalibrated sparse-view images (i.e., as few as 2-8 inputs), which is a cha…

cs.CV2025

Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation

Yuanbo Yang, Jiahao Shao, Xinyang Li +3

In this work, we introduce Prometheus, a 3D-aware latent diffusion model for text-to-3D generation at both object and scene levels in seconds. We formulate 3D scene generation as m…

cs.CV2024

PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes

Bin Tan, Rui Yu, Yujun Shen +1

This paper presents PlanarSplatting, an ultra-fast and accurate surface reconstruction approach for multiview indoor images. We take the 3D planes as the main objective due to thei…

cs.CV2024★ 1 cited

Learning Temporally Consistent Video Depth from Video Diffusion Priors

Jiahao Shao, Yuanbo Yang, Hongyu Zhou +6

This work addresses the challenge of streamed video depth estimation, which expects not only per-frame accuracy but, more importantly, cross-frame consistency. We argue that sharin…

cs.CV2024★ 4 cited

InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior

Zhiheng Liu, Hao Ouyang, Qiuyu Wang +7

3D Gaussians have recently emerged as an efficient representation for novel view synthesis. This work studies its editability with a particular focus on the inpainting task, which…

cs.CV2024

SpatialTracker: Tracking Any 2D Pixels in 3D Space

Yuxi Xiao, Qianqian Wang, Shangzhan Zhang +4

Recovering dense and long-range pixel motion in videos is a challenging problem. Part of the difficulty arises from the 3D-to-2D projection process, leading to occlusions and disco…

cs.CV2024★ 2 cited

MaPa: Text-driven Photorealistic Material Painting for 3D Shapes

Shangzhan Zhang, Shangzan Zhang, Sida Peng +8

This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural mat…

cs.CV2024

Learning 3D-Aware GANs from Unposed Images with Template Feature Field

Xinya Chen, Hanlei Guo, Yanrui Bin +5

Collecting accurate camera poses of training images has been shown to well serve the learning of 3D-aware generative adversarial networks (GANs) yet can be quite expensive in pract…