UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation
Qin Guo, Ailing Zeng, Dongxu Yue, Ceyuan Yang, Yang Cao, Hanzhong Guo, Fei Shen, Wei Liu, Xihui Liu, Dan Xu
Abstract
UNIMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation ness of UNIMC, particularly in heavy occlusions and multi-class scenarios. Project page can be found at this link. (A) Raw image (B) Most condition form (C) Our condition form Class name: … Keypoint: … Bounding box: … Keypoint binding confusion Class binding confusion (A) Raw image (B) Most condition form (C) Our condition form Keypoint binding confusion Class binding confusion ☹ 🤗
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Cited by top-tier papers3
- M4V: Multimodal Mamba for Efficient Text-to-Video GenerationJiancheng Huang, Gengwei Zhang, Zequn Jie, Siyu Jiao et al.CVPR 2026 · 14 citations
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- OmniPortrait: Fine-Grained Personalized Portrait Synthesis via Pivotal OptimizationDongxu Yue, Bo Lin, Yao Tang, Jiajun Liang et al.ICLR 2026
Builds on31
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- Segment AnythingAlexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao et al.ICCV 2023 · 13,211 citations
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 13,211 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
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