Recent advances in generative adversarial networks (GANs) have provided potential solutions for photo-realistic human image synthesis. However, the explicit and individual control of synthesis over multiple factors, such as poses, body shapes, and skin colors, remains difficult for existing methods. This is because current methods mainly rely on a single pose/appearance model, which is limited in dis-entangling various poses and appearance in human images. In addition, such a unimodal strategy is prone to causing severe artifacts in the generated images like color distortions and unrealistic textures. To tackle these issues, this paper proposes a multi-factor conditioned method dubbed BodyGAN. Specifically, given a source image, our Body-GAN aims at capturing the characteristics of the human body from multiple aspects: (i) A pose encoding branch consisting of three hybrid subnetworks is adopted, to generate the semantic segmentation based representation, the 3D surface based representation, and the key point based rep-resentation of the human body, respectively. (ii) Based on the segmentation results, an appearance encoding branch is used to obtain the appearance information of the human body parts. (iii) The outputs of these two branches are represented by user-editable condition maps, which are then processed by a generator to predict the synthesized image. In this way, our BodyGAN can achieve the fine-grained dis-entanglement of pose, body shape, and appearance, and consequently enable the explicit and effective control of syn-thesis with diverse conditions. Extensive experiments on multiple datasets and a comprehensive user study show that our BodyGAN achieves the state-of-the-art performance.

Recovering dense human poses from images plays a critical role in establishing an image-to-surface correspondence between RGB images and the 3D surface of the human body, serving the foundation of rich real-world applications, such as virtual humans, monocular-to-3d reconstruction. However, the popular DensePose-COCO dataset relies on a sophisticated manual annotation system, leading to severe limitations in acquiring the denser and more accurate annotated pose resources. In this work, we introduce a new 3D human-body model with a series of decoupled parameters that could freely control the generation of the body. Furthermore, we build a data generation system based on this decoupling 3D model, and construct an ultra dense synthetic benchmark UltraPose, containing around 1.3 billion corresponding points. Compared to the existing manually annotated DensePose-COCO dataset, the synthetic UltraPose has ultra dense image-to-surface correspondences without annotation cost and error. Our proposed UltraPose provides the largest benchmark and data resources for lifting the model capability in predicting more accurate dense poses. To promote future researches in this field, we also propose a transformer-based method to model the dense correspondence between 2D and 3D worlds. The proposed model trained on synthetic UltraPose can be applied to real-world scenarios, indicating the effectiveness of our benchmark and model.1

Abstract Multi‐person pose and shape estimation is very challenging, especially when the persons have close interactions. Existing methods only work well when people are well spaced out in the captured images. However, close interaction among people is very common in real life, which is more challenge due to complex articulation, frequent occlusion and inherent ambiguities. We present a fully‐automatic markerless motion capture method to simultaneously estimate 3D poses and shapes of closely interacting people from multi‐view sequences. We first predict the 2D joints for each person in an image, and then design a spatio‐temporal tracker for multi‐person pose tracking based on multi‐view videos. Finally, we estimate 3D poses and shapes of all the persons with multi‐view constraints using a skinned multi‐person linear model (SMPL). Experimental results demonstrate that our method achieves fast but accurate pose and shape estimation results for multi‐person close interaction cases. Compared with existing methods, our method does not need pre‐segmentation for each person and manual intervention, which greatly reduces the complexity of the system including time complexity and system processing complexity.