Jonathan Hans Soeseno, Ying-Sheng Luo, Trista Pei-chun Chen, Wei-Chao Chen
{"title":"Transition Motion Tensor: A Data-Driven Approach for Versatile and Controllable Agents in Physically Simulated Environments","authors":"Jonathan Hans Soeseno, Ying-Sheng Luo, Trista Pei-chun Chen, Wei-Chao Chen","doi":"10.1145/3478512.3488599","DOIUrl":"https://doi.org/10.1145/3478512.3488599","url":null,"abstract":"This paper proposes the Transition Motion Tensor, a data-driven framework that creates novel and physically accurate transitions outside of the motion dataset. It enables simulated characters to adopt new motion skills efficiently and robustly without modifying existing ones. Given several physically simulated controllers specializing in different motions, the tensor serves as a temporal guideline to transition between them. Through querying the tensor for transitions that best fit user-defined preferences, we can create a unified controller capable of producing novel transitions and solving complex tasks that may require multiple motions to work coherently. We apply our framework on both quadrupeds and bipeds, perform quantitative and qualitative evaluations on transition quality, and demonstrate its capability of tackling complex motion planning problems while following user control directives.","PeriodicalId":156290,"journal":{"name":"SIGGRAPH Asia 2021 Technical Communications","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123202193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Real Time Cluster Path Tracing","authors":"Feng Xie, Petro Mishchuk, W. Hunt","doi":"10.1145/3478512.3488605","DOIUrl":"https://doi.org/10.1145/3478512.3488605","url":null,"abstract":"Photorealistic rendering effects are common in films, but most real time graphics today still rely on scan-line based multi-pass rendering to deliver rich visual experiences. While there have been prior works in distributed path tracing for static scene and objects under rigid motion, real time path tracing of deforming characters has to support per-frame dynamic BVH changes. We present the architecture and implementation of the first real-time production quality cluster path tracing renderer that supports film quality animation and deformation. We build our cluster path tracing system using the open source Blender and its GPU accelerated production quality renderer Cycles. Our system's rendering performance and quality scales linearly with the number of RTX cluster nodes used. It is able to generate and deliver path traced images with global illumination effects to remote light-weight client systems at 15-30 frames per second for a variety of Blender scenes including animated digital human characters with skin deformation and virtual objects.","PeriodicalId":156290,"journal":{"name":"SIGGRAPH Asia 2021 Technical Communications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115203540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Autocomplete Repetitive Stroking with Image Guidance","authors":"Yilan Chen, Kin Chung Kwan, Li-Yi Wei, Hongbo Fu","doi":"10.1145/3478512.3488595","DOIUrl":"https://doi.org/10.1145/3478512.3488595","url":null,"abstract":"Image-guided drawing can compensate for the lack of skills but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present a method to autocomplete repetitive short strokes during users’ normal drawing process. Users can draw over a reference image as usual. At the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow users’ input style when certain repetition is detected. Our key idea is to jointly analyze image regions and operation history for detecting and predicting repetitions. The proposed system can reduce tedious repetitive inputs while being fully under user control.","PeriodicalId":156290,"journal":{"name":"SIGGRAPH Asia 2021 Technical Communications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131811068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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