most citedUniTEX: Universal High Fidelity Generative Texturing for 3D Shapes

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cs.CV2026

AutoWeather4D: Autonomous Driving Video Weather Conversion via G-Buffer Dual-Pass Editing

Tianyu Liu, Weitao Xiong, Kunming Luo +4

Generative video models have significantly advanced the photorealistic synthesis of adverse weather for autonomous driving; however, they consistently demand massive datasets to le…

cs.CV2025

SceneMaker: Open-set 3D Scene Generation with Decoupled De-occlusion and Pose Estimation Model

Yukai Shi, Weiyu Li, Zihao Wang +4

We propose a decoupled 3D scene generation framework called SceneMaker in this work. Due to the lack of sufficient open-set de-occlusion and pose estimation priors, existing method…

cs.CV20251 cited

UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes

Yixun Liang, Kunming Luo, Xiao Chen +5

We present UniTEX, a novel two-stage 3D texture generation framework to create high-quality, consistent textures for 3D assets. Existing approaches predominantly rely on UV-based i…

cs.CV20251 cited

Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets

Weiyu Li, Xuanyang Zhang, Zheng Sun +15

While generative artificial intelligence has advanced significantly across text, image, audio, and video domains, 3D generation remains comparatively underdeveloped due to fundamen…

cs.CV2024

Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders

Rui Chen, Jianfeng Zhang, Yixun Liang +7

Recent 3D content generation pipelines commonly employ Variational Autoencoders (VAEs) to encode shapes into compact latent representations for diffusion-based generation. However,…