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Haoge Deng I am an incoming PhD student jointly supervised by the Institute of Automation, Chinese Academy of Sciences (CASIA), and Beijing Academy of Artificial Intelligence (BAAI), under the supervision of Prof Zhaoxiang Zhang and Dr. Xinlong Wang. I am also a final-year MPhil student at BUPT in China, supervised by Prof. Yonggang Qi. Prior to this, I obtained my Bachelor's degree in Electronics Information Science and Technology from BUPT in 2022. My research interests revolve around computer visionn and generative models , with a particular focus on image generation , video generation , and 3D content creation . |
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ResearchRepresentative papers are highlighted. * indicates equal contribution, # indicates corresponding author. |
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Autoregressive Video Generation without Vector Quantization
Haoge Deng*, Ting Pan*, Haiwen Diao*, Zhengxiong Luo*, Yufeng Cui, Huchuan Lu, Shiguang Shan, Yonggang Qi#, Xinlong Wang# International Conference on Learning Representations (ICLR, TH-CPL A), 2025 [arxiv] | [project page] | [code] | [openreview] | [post] | [iclr poster] [hugging face 🤗 daily papers] NOVA is a non-quantized autoregressive model that enables efficient video generation by reformulating the video creation as frame-by-frame and set-by-set predictions. |
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You See it, You Got it: Learning 3D Creation on Pose-Free Videos at
Scale
Baorui Ma*, Huachen Gao*, Haoge Deng*, Zhengxiong Luo, Tiejun Huang, Lulu Tang#, Xinlong Wang# IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR, CCF-A), 2025 (Highlights, ~3% acceptance rate) [arxiv] | [project page] | [code] | [dataset] | [post] | [hugging face 🤗 daily papers] See3D is a scalable visual-conditional MVD model for open-world 3D creation, which can be trained on web-scale video collections without camera pose annotations. |
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GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and
Consistent 3D Generation
Baorui Ma*, Haoge Deng*, Junsheng Zhou , Yu-Shen Liu, Tiejun Huang, Xinlong Wang# arXiv, 2023 [arXiv] | [project page] | [code] GeoDream is a 3D generation method that integrates explicit generalized 3D priors with 2D diffusion priors to enhance the capability of obtaining unambiguous 3D consistent geometric structures without sacrificing diversity or fidelity. |
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SketchKnitter: Vectorized Sketch Generation with Diffusion Models
Qiang Wang, Haoge Deng, Yonggang Qi#, Da Li, Yi-Zhe Song, International Conference on Learning Representations (ICLR, TH-CPL A), 2023 (Spotlight, ~5% acceptance rate) [paper] | [openreview] | [code] SketchKnitter is a method that achieves vectorized sketch generation by reversing the stroke deformation process using a diffusion model learned from real sketches, enabling the creation of higher quality, visually appealing sketches with fewer sampling steps. |
Education |
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Master of Science in Artificial Intelligence Under the supervision of Prof. Yonggang Qi Sept. 2022 ~ July. 2025 (Expected) |
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Bachelor of Engineering in Electronics Engineering Outstanding Graduate of Beijing Province Sept. 2018 ~ July. 2022 |
Experiences |
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Research Intern at BAAI-Vision Research on Video generation Advised by Dr. Zhengxiong Luo and Dr. Xinlong Wang Jun. 2024 ~ Now |
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Research Intern at BAAI-Vision Research on 3D content generation Advised by Dr. Baorui Ma and Dr. Xinlong Wang Jun. 2023 ~ Jun. 2024 |
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Research Intern at Meituan-AutoML Research on Lidar lidar range generation Advised by Dr. Zhi Tian and Dr. Xiangxiang Chu Jan. 2023 ~ Jun. 2023 |
Academic Services
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