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chen geng stanford cs chen geng my first name is chen and my last name is geng i prefer to be addressed by my first name chen i also go by ken sometimes possible pronunciation chen ch uhn geng guh ng hi i m a cs ph d student at stanford fortunate to be advised by prof jiajun wu my research lies at the intersection of 4d computer vision graphics and machine learning i m broadly interested in data driven modeling of the physical world and applications of such models in robotics and natural science currently i m obsessed with developing neural simulators for the inverse modeling of macroscopic mechanical systems my research is in part supported by an nvidia graduate fellowship previously i got my bachelor s degree in computer science from zhejiang university in 2023 with an honors degree from chu kochen honors college during my undergraduate i was privileged to work closely with prof xiaowei zhou and prof sida peng on several research projects i spent a wonderful summer at stanford also with the cogai group in 2022 if you have shared research interests or have any topics you d like to chat about especially if you re from underrepresented groups don t hesitate to shoot me an email i m always up for exploring potential collaborations and or engaging in insightful conversations email x y where x gengchen y cs stanford edu google scholar nbsp nbsp x nbsp nbsp bluesky nbsp nbsp github nbsp nbsp linkedin recent news 04 2026 invited talk at the simulation intelligence group at cmu on rethinking inverse simulation through the lens of lagrangian mechanics 02 2026 four papers are accepted to cvpr 2026 01 2026 one paper is accepted to iclr 2026 01 2026 we are organizing the 2nd workshop on 4d vision at cvpr 2026 submit your work on 4d vision to our workshop and share it with the community 01 2026 this year i will co organize the stanford vision seminar subscribe to our mailing list here 12 2025 excited to announce that i received the nvidia graduate fellowship for 2026 2027 04 2025 we will be organizing the workshop on generating digital twins from images and videos at iccv 2025 04 2025 our paper birth and death of a rose is selected as an oral presentation at cvpr 2025 03 2025 one paper is accepted to siggraph 2025 02 2025 two papers are accepted to cvpr 2025 02 2024 one paper is accepted to cvpr 2024 01 2024 one paper is accepted to iclr 2024 recent research show selected show all nbsp nbsp nbsp nbsp denotes equal contribution denotes student co mentored representative works are highlighted nbsp nbsp nbsp nbsp for the comprehensive list check out my google scholar page your browser does not support the video tag neurok generative 4d neural object kinematics chen geng guangzhao he yue gao yunzhi zhang shangzhe wu jiajun wu cvpr 2026 paper project page tl dr we turn any static 3d shape into an interactable 4d asset with a neural simulator built on the minimal inductive bias of lagrangian mechanics choreographing a world of dynamic objects yanzhe lyu chen geng karthik dharmarajan yunzhi zhang hadi alzayer shangzhe wu jiajun wu cvpr 2026 paper project page tl dr we propose a universal pipeline for generating 4d scenes composed of dynamic objects by distilling from video generative models art articulated reconstruction transformer zizhang li cheng zhang zhengqin li henry howard jenkins zhaoyang lv chen geng jiajun wu richard newcombe jakob engel zhao dong cvpr 2026 paper project page tl dr a transformer based model for reconstructing articulated dynamic objects from images your browser does not support the video tag coupled diffusion sampling for training free multi view image editing hadi alzayer yunzhi zhang chen geng jia bin huang jiajun wu cvpr 2026 paper project page tl dr multi view image editing by sampling from two diffusion models of different modalities concurrently genfusion feed forward human performance capture via progressive canonical space updates youngjoong kwon yao he heejung choi chen geng zhengmao liu jiajun wu ehsan adeli iclr 2026 paper project page tl dr a feed forward method for human performance capture that progressively updates a canonical space with incoming monocular rgb frames using probabilistic regression to produce sharp novel view renderings anymate a dataset and baselines for learning 3d object rigging yufan deng yuhao zhang chen geng shangzhe wu jiajun wu siggraph 2025 paper project page demo code arxiv tl dr we propose a dataset and benchmark for supervised learning based 4d object rigging methods category agnostic neural object rigging guangzhao he chen geng shangzhe wu jiajun wu cvpr 2025 paper project page arxiv tl dr we discover animatable motion subspaces for any 4d objects your browser does not support the video tag birth and death of a rose chen geng yunzhi zhang shangzhe wu jiajun wu cvpr 2025 oral presentation 3 3 of the accepted papers paper project page code coming soon arxiv tl dr we generate temporal 4d object intrinsics from 2d foundation models your browser does not support the video tag relightable and animatable neural avatar from sparse view video zhen xu sida peng chen geng linzhan mou zihan yan jiaming sun hujun bao xiaowei zhou cvpr 2024 highlight 11 9 of the accepted papers paper project page code arxiv video tl dr we estimate physically based intrinsics of dynamic characters from monocular videos neural polynomial gabor fields for macro motion analysis chen geng hong xing koven yu sida peng xiaowei zhou jiajun wu iclr 2024 paper project page code coming soon openreview tl dr we discover a low dimensional interpretable motion representation for dynamic scenes with macro motion your browser does not support the video tag tree structured shading decomposition chen geng hong xing koven yu sharon zhang maneesh agrawala jiajun wu iccv 2023 paper project page video code marktechpost tl dr we decompose the shading of objects into a tree structured representation which can be edited or interpreted by users easily abstract we study the problem of obtaining a tree structured representation for shading objects prior work typically uses the parametric or measured representation to model shading which is neither interpretable nor easily editable our method uses the shade tree representation which combines basic shading nodes and compositing methods to model and decomposes the material shading such a representation enables users to edit previously rigid material appearances in an efficient and intuitive manner in particular novice users who are unfamiliar with the construction of such shade trees can quickly obtain such a representation the extraction of such a representation enables the editing and understanding of object shading even for novice users the biggest challenge in this task is that the discrete structure of the shade tree is not differentiable we propose a hybrid algorithm to address this issue first given an input image a recursive amortized inference model is leveraged to initialize a guess of the tree structure and corresponding leaf node parameters then we apply an optimization based method to fine tune the result experiments show that our method works well on synthetic images realistic images and non realistic vector drawings surpassing the baselines significantly your browser does not support the video tag learning neural volumetric representations of dynamic humans in minutes chen geng sida peng zhen xu hujun bao xiaowei zhou cvpr 2023 paper project page code tl dr we accelerate the learning of neural volumetric videos of dynamic humans by over 100 times abstract this paper addresses the challenge of quickly reconstructing free viewpoint videos of dynamic humans from sparse multi view videos some recent works represent the dynamic human as a canonical neural radiance field nerf and a motion field which are learned from videos through differentiable rendering they generally require a lengthy optimization process other generalization methods leverage learned prior from datasets and reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity in this paper we propose a novel method for creating viewpoint free human performance synthesis from sparse view videos in minutes with competitive visual quality specifically we leverage the human body prior to define a novel part based voxelized nerf representation which distributes the representational power of the canonical human model efficiently furthermore we propose a novel dimensionality reduction 2d motion parameterization scheme to increase the convergence rate of the human deformation field experiments demonstrate that our approach can be trained 100 times faster than prior per scene optimiztion methods while being competitive in the rendering quality we show that given a video capturing a human performer of 100 frames our model typically takes about 5 minutes for training to produce photorealistic free viewpoint videos on a single rtx 3090 gpu the code will be released for reproducibility your browser does not support the video tag implicit neural representations with structured latent codes for human body modeling sida peng chen geng yuanqing zhang yinghao xu qianqian wang qing shuai xiaowei zhou hujun bao tpami 2023 paper code ieee xplore tl dr our approach reconstruct geometry and appearance of human performers with high accuracy from sparse observations novel view synthesis of human interactions from sparse multi view videos qing shuai chen geng qi fang sida peng wenhao shen xiaowei zhou hujun bao siggraph 2022 featured in the technical paper trailer paper bibtex code project page tl dr given sparse multi view videos of crowded scenes with multiple human performers our approach is able to generate high fidelity novel views and accurate instance masks inproceedings multinb nbsp nbsp nbsp nbsp title novel view synthesis of human interactions from sparse multi view videos nbsp nbsp nbsp nbsp author qing shuai and chen geng and qi fang and sida peng and wenhao shen and xiaowei zhou and hujun bao nbsp nbsp nbsp nbsp booktitle siggraph conference proceedings nbsp nbsp nbsp nbsp year 2022 experience stanford university 2023 present stanford california phd candidate in computer science advisor prof jiajun wu zhejiang university 2019 2023 hangzhou china b eng honours in computer science cumulative gpa 94 38 100 3 99 4 0 major gpa 96 67 100 4 0 4 0 advisor prof xiaowei zhou professional activities ️ conference reviewer cvpr 2024 eccv 2024 iccv 2025 corl 2026 siggraph 2025 2026 siggraph asia 2024 2025 icml 2024 position papers for 2026 iclr 2025 neurips 2024 neurips workshop proposals 2025 neurips d b 2024 icra 2024 3dv 2023 aaai 2024 pacific graphics 2024 journal reviewer ieee robotics and automation letters ra l acm transactions on graphics tog ieee transactions on pattern analysis and machine intelligence t pami ieee transactions on visualization and computer graphics tvcg co organizer stanford vision seminar 2026 stanford gcafé graphics lunch seminar 2024 3dv 2025 cvpr 2026 workshop on 4d vision iccv 2025 workshop on generating digital twins from images and videos mentorship and service faculty student liasion at stanford cs 2026 stanford phd application support program for underrepresented group sasp 2023 stanford cs undergraduate mentoring program 2023 misc finalist qualcomm innovation fellowship 2024 this website is adapted from this awesome template last updated feb 2026