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GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation

science

:2409.11689v1 Announce Type: cross Abstract: Pose skeleton images are an important reference in pose-controllable image generation. In order to enrich the source of skeleton images, recent works have investigated the generation of pose skeletons based on natural language. These methods are based on GANs. However, it remains challenging to perform diverse, structurally correct and aesthetically pleasing human pose skeleton generation with various textual inputs. To address this problem, we propose a framework with GUNet as the main model, PoseDiffusion. It is the first generative framework based on a diffusion model and also contains a series of variants fine-tuned based on a stable diffusion model.

arXiv     Sep 19, 2024