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Found 3 papers, 2 papers with code
M$^3$Face: A Unified Multi-Modal Multilingual Framework for Human Face Generation and Editing
Additionally, we propose the M3CelebA Dataset, a large-scale multi-modal and multilingual face dataset containing high-quality images, semantic segmentations, facial landmarks, and different captions for each image in multiple languages.
Benchmarking Robustness to Text-Guided Corruptions
Unlike other works that use synthetic or hand-picked data for benchmarking, we use diffusion models as they are generative models capable of learning to edit images while preserving their semantic content.
A generic diffusion-based approach for 3D human pose prediction in the wild
Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlusions.
Ranked #1 on
Human Pose Forecasting
on HumanEva-I
