iTryOn is a diffusion-based framework that adds spatial 3D hand guidance and semantic action-aware embeddings to handle complex garment deformations during human-clothing interactions in videos.
arXiv preprint arXiv:2104.11222 , year=
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Latent diffusion models achieve state-of-the-art inpainting and competitive results on unconditional generation, scene synthesis, and super-resolution by performing the diffusion process in the latent space of pretrained autoencoders with cross-attention conditioning, while cutting computational and
Diffusion models with architecture improvements and classifier guidance achieve superior FID scores to GANs on unconditional and conditional ImageNet image synthesis.
Latte achieves state-of-the-art video generation on FaceForensics, SkyTimelapse, UCF101, and Taichi-HD by using a latent diffusion transformer with four efficient spatial-temporal decomposition variants and best-practice training choices.
citing papers explorer
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iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance
iTryOn is a diffusion-based framework that adds spatial 3D hand guidance and semantic action-aware embeddings to handle complex garment deformations during human-clothing interactions in videos.
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High-Resolution Image Synthesis with Latent Diffusion Models
Latent diffusion models achieve state-of-the-art inpainting and competitive results on unconditional generation, scene synthesis, and super-resolution by performing the diffusion process in the latent space of pretrained autoencoders with cross-attention conditioning, while cutting computational and
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Diffusion Models Beat GANs on Image Synthesis
Diffusion models with architecture improvements and classifier guidance achieve superior FID scores to GANs on unconditional and conditional ImageNet image synthesis.
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Latte: Latent Diffusion Transformer for Video Generation
Latte achieves state-of-the-art video generation on FaceForensics, SkyTimelapse, UCF101, and Taichi-HD by using a latent diffusion transformer with four efficient spatial-temporal decomposition variants and best-practice training choices.