We, the Editors and Publisher of the International Journal of Human–Computer Interaction have retracted the following article:Ning, C., Li, M., & Jiang, S (2024). Physical Education Using VR Mobile Apps: Development of Individual Thinking Skills and Self-Esteem. International Journal of Human–Computer Interaction, 41(16), 10339–10349. https://doi.org/10.1080/10447318.2024.2433593Following publication, concerns were raised by a third party about the methods and results described in the article. The publisher agreed with the concerns and contacted the authors for an explanation; however, the authors did not respond. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article.We have been informed in our decision-making by our editorial policies and the COPE guidelines.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted.”

Recent years have witnessed the tremendous progress of 3D GANs for generating view-consistent radiance fields with photo-realism. Yet, high-quality generation of human radiance fields remains challenging, partially due to the limited human-related priors adopted in existing methods. We present HumanGen, a novel 3D human generation scheme with detailed geometry and 360° realistic free-view rendering. It explicitly marries the 3D human generation with various priors from the 2D generator and 3D reconstructor of humans through the design of “anchor image”. We introduce a hybrid feature representation using the anchor image to bridge the latent space of HumanGen with the existing 2D generator. We then adopt a pronged design to disentangle the generation of geometry and appearance. With the aid of the anchor image, we adapt a 3D reconstructor for fine-grained details synthesis and propose a two-stage blending scheme to boost appearance generation. Extensive experiments demonstrate our effectiveness for state-of-the-art 3D human generation regarding geometry details, texture quality, and free-view performance. Notably, HumanGen can also incorporate various off-the-shelf 2D latent editing methods, seamlessly lifting them into 3D.

4D modeling of human-object interactions is critical for numerous applications. However, efficient volumetric capture and rendering of complex interaction scenarios, especially from sparse inputs, remain challenging. In this paper, we propose NeuralHOFusion, a neural approach for volumetric human-object capture and rendering using sparse consumer RGBD sensors. It marries traditional non-rigid fusion with recent neural implicit modeling and blending advances, where the captured humans and objects are layer-wise disentangled. For geometry modeling, we propose a neural implicit inference scheme with non-rigid key-volume fusion, as well as a template-aid robust object tracking pipeline. Our scheme enables detailed and complete geometry generation under complex interactions and occlusions. Moreover, we introduce a layer-wise human-object texture rendering scheme, which combines volumetric and image-based rendering in both spatial and temporal domains to obtain photo-realistic results. Extensive experiments demonstrate the effectiveness and efficiency of our approach in synthesizing photo-realistic free-view results under complex human-object interactions.