Existing motion generation methods based on MoCap data are often limited by data quality and coverage. In this work, we propose a framework that generates diverse, physically feasible full-body human reaching and grasping motions using only brief walking MoCap data. Based on the observation that walking data captures valuable movement patterns transferable across tasks and, on the other hand, the advanced kinematic methods can generate diverse grasping poses, which can then be interpolated into motions to serve as task-specific guidance. Our approach incorporates an active data generation strategy to maximize the utility of the generated motions, along with a local feature alignment mechanism that transfers natural movement patterns from walking data to enhance both the success rate and naturalness of the synthesized motions. By combining the fidelity and stability of natural walking with the flexibility and generalizability of task-specific generated data, our method demonstrates strong performance and robust adaptability in diverse scenes and with unseen objects.

Abstract High‐dynamic‐range image (HDRI) displays scenes as vivid as the real scenes. HDRI can be reconstructed by fusing a set of bracketed‐exposure low‐dynamic‐range images (LDRI). For the reconstruction, many works succeed in removing the ghost artefacts caused by moving objects. The critical issue is reconstructing the areas which are saturated due to bad exposure and occluded due to motion with no ghost artefacts. To overcome this issue, this paper proposes texture and exposure awareness based refill. The proposed work first locates the saturated and occluded areas existing in input image set, then refills background textures or patches containing rough exposure and colour information into located areas. Proposed work can be integrated with multiple existing ghost removal works to improve the reconstruction result. Experimental results show that proposed work removes the ghost artefacts caused by saturated and occluded areas in subjective evaluation. For the objective evaluation, the proposed work improves the HDR‐VDP‐2 evaluation result for multiple conventional works by 1.33% on average.

Ghost-like artifact caused by ill-exposed and motion areas is one of the most challenging problems in high dynamic range (HDR) image reconstruction. When the motion range is small, previous methods based on optical flow or patch-match can suppress ghost-like artifacts by first aligning input images before merging them. However, they are not robust enough and still produce artifacts for challenging scenes where large foreground motions exist. To this end, we propose a deep network with an attention module and motion-emphasized loss function to produce ghost-free HDR images. In the attention module, we use the channel and spatial attention to guide the network to emphasize important components such as motion and saturated areas automatically. To be robust to images with different resolutions and objects with distinct scales, we adopt the selective kernel network as the basic framework for channel attention. In addition to the attention module, the motion-emphasized loss function based on the motion and ill-exposed areas mask is designed to help the network reconstruct motion areas. Experiments on the public dataset indicate that the proposed SK-AHDRNet produces ghost-free results where detail in ill-exposed areas is well recovered. The proposed method scores 43.17 with PSNR metric and 61.02 with HDR-VDP-2 metric on test which outperforms all conventional works. According to quantitative and qualitative evaluations, the proposed method can achieve state-of-the-art performance.