3D Generation

Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation
We propose a two-stage approach named Hunyuan3D-1.0 including a lite version and a standard version, that both support text- and image-conditioned generation. In the first stage, we employ a multi-view diffusion model that efficiently generates multiview RGB in approximately 4 seconds. In the second stage, we introduce a feedforward reconstruction model that rapidly and faithfully reconstructs the 3D asset given the generated multi-view images in approximately 7 seconds.
Xianghui Yang, Huiwen Shi*, Bowen Zhang*, Fan Yang, Jiacheng Wang, Hongxu Zhao, Xinhai Liu,, Xinzhou Wang, Qingxiang Lin, Jiaao Yu, Lifu Wang, Zhuo Chen, Sicong Liu,, Yuhong Liu, Yong Yang, Di Wang, Jie Jiang, Chunchao Guo
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Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation
ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
Novel-view Generation, 3D Generation, Diffusion Model
Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, Anton van den Hengel
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ViewFusion: Towards Multi-View Consistency via Interpolated Denoising