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Diffusion models have achieved remarkable success in generating realistic images but suffer from generating accurate human hands, such as incorrect finger counts or irregular shapes.
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Diffusion Models Beat GANs on Image Synthesis. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 8780–8794
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ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
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SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
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Alexander Kapitanov, Andrew Makhlyarchuk, and Karina Kvanchiani. 2022 · 2022
Cited alongside, same era.
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. In International Conference on Machine Learning
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RePaint: Inpainting using Denoising Diffusion Probabilistic Models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022 · 2022
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 162) , Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.). PMLR, 16784–16804
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Hierarchical Text-Conditional Image Generation with CLIP Latents
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Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer
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High-Resolution Image Synthesis with Latent Diffusion Models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. In Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 36479–36494
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Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. 2023 · 2023
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Generating Images of Rare Concepts Using Pre-trained Diffusion Models
Dvir Samuel, Rami Ben-Ari, Simon Raviv, Nir Darshan, and Gal Chechik. 2023 · 2023
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Diffusion-HPC: Generating Synthetic Images with Realistic Humans
Zhenzhen Weng, Laura Bravo-Sánchez, and Serena Yeung. 2023 · 2023
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Affordance Diffusion: Synthesizing Hand-Object Interactions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Yufei Ye, Xueting Li, Abhinav Gupta, Shalini De Mello, Stan Birchfield, Jiaming Song, Shubham Tulsiani, and Sifei Liu. 2023 · 2023
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Adding Conditional Control to Text-to-Image Diffusion Models. In Proceedings of the IEEE International Conference on Computer Vision
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023 · 2023
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Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models. In Advances In Neural Information Processing Systems
Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, and Kwan-Yee K Wong. 2023 · 2023
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Midjourney
2024 · 2024
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HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Supreeth Narasimhaswamy, Uttaran Bhattacharya, Xiang Chen, Ishita Dasgupta, Saayan Mitra, and Minh Hoai. 2024 · 2024
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