Junyi42 Junyi Zhang Github

By themelower On Apr 14, 2026

Junyi Shen Github A graph representing junyi42's contributions from april 06, 2025 to april 09, 2026. the contributions are 100% commits, 0% pull requests, 0% issues, 0% code review. Previously, i obtained my b.eng. (honors) in computer science from shanghai jiao tong university. i have interned at google deepmind and microsoft research asia. my research interests mainly lie in computer vision, deep generative models, 4d, and robotics. email cv google scholar twitter github.

Junyi Jiang Jiang Junyi Github About hi, i'm currently an 1st year phd student at uc berkeley. for more information about me, please visit my academic webpage junyi42. Arxiv:2305.15347 arxiv:2303.11589 models 1 junyi42 layoutdiffusion updated jun 29, 2023 • 3 datasets none public yet. 🎮 we release visgym: diverse, customizable, scalable environments for multimodal agents (w @junyi42 @aomaru 21490) 🌐 with 17 environments across multiple domains, we show systematically the brittleness of vlms in visual interaction, and what training leads to. Cs ph.d. student @bair, b.eng. @sjtu cse. computer vision and deep generative models. working with @google research. prev. @mvig sjtu, @microsoft research junyi42.

Junyi42 Junyi Zhang Github 🎮 we release visgym: diverse, customizable, scalable environments for multimodal agents (w @junyi42 @aomaru 21490) 🌐 with 17 environments across multiple domains, we show systematically the brittleness of vlms in visual interaction, and what training leads to. Cs ph.d. student @bair, b.eng. @sjtu cse. computer vision and deep generative models. working with @google research. prev. @mvig sjtu, @microsoft research junyi42. Loger processes long video streams in chunks with a hybrid memory design to improve large scale geometric reconstruction quality and consistency. Junyi42 follow miguelvalverde's profile picturedark pen's profile picturereza2kn's profile picture 6 followers · 2 following junyi42 junyi42 junyi42. Cs ph.d. student @bair, b.eng. @sjtu cse. computer vision and deep generative models. working with @google research. prev. @mvig sjtu, @microsoft research junyi42. Monst3r processes a dynamic video to produce a time varying dynamic point cloud, along with per frame camera poses and intrinsics, in a predominantly feed forward manner. this representation then enables the efficient computation of downstream tasks, such as video depth estimation and dynamic static scene segmentation.

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