Approaches for automatic low-dimensional human shape refinement with priors or generic cues using RGB-D data ?
A novel and accurate method to refine low dimensional human shape using RGB-D data is proposed. Uses of multiple modalities do not carry any features from the shape provider. Combines low and high level observations jointly in multi-layer graph structure Extensive experiments showed that it outperforms compared suitable algorithms. Also, an existing method is extended by fusing more generic cues for this purpose.
rgb-d human shape refinement using rgb-d data multi-layer graph cut human body shape descriptor random decision forests refinement of low-dimensional representations