Junchen Liu (@junchenliu77) 's Twitter Profile
Junchen Liu

@junchenliu77

Research intern at @berkeley_ai

ID: 1572092935252111360

linkhttps://junchenliu77.github.io calendar_today20-09-2022 05:19:13

9 Tweet

68 Takipçi

357 Takip Edilen

HU, Wenbo (@wbhu_cuhk) 's Twitter Profile Photo

Excited to share our #TrajectoryCrafter, a diffusion model for Redirecting Camera Trajectory in Monocular Videos! Try to explore the world underlying your videos~ Page: trajectorycrafter.github.io Demo: huggingface.co/spaces/Doubiiu… Code: github.com/TrajectoryCraf…

Junyi Zhang (@junyi42) 's Twitter Profile Photo

Introducing St4RTrack!🖖 Simultaneous 4D Reconstruction and Tracking in the world coordinate feed-forwardly, just by changing the meaning of two pointmaps! st4rtrack.github.io

Arthur Allshire (@arthurallshire) 's Twitter Profile Photo

our new system trains humanoid robots using data from cell phone videos, enabling skills such as climbing stairs and sitting on chairs in a single policy (w/ Hongsuk Benjamin Choi Junyi Zhang David McAllister)

Chung Min Kim (@chungminkim) 's Twitter Profile Photo

Excited to introduce PyRoki ("Python Robot Kinematics"): easier IK, trajectory optimization, motion retargeting... with an open-source toolkit on both CPU and GPU

Ruilong Li (@ruilong_li) 's Twitter Profile Photo

🌟gsplat🌟just integrated 3DGUT, which allows training and rendering 3DGS on *distorted* pinhole/fisheye cameras, as well as rolling shutter effects! > Checkout this NVIDIA tech blog developer.nvidia.com/blog/revolutio… > Sweepstakes to win a 4090 nvidia.com/en-us/research…

Mingxuan Wu (@jackwal97390450) 's Twitter Profile Photo

Introducing POD ! Predict-Optimize-Distill : A Self-Improving Cycle for 4D Object Understanding ! Inputs: a multi-view scan of an object + casually captured, long-form human interaction monocular videos (from your phone) ! Outputs: 3D part poses over time .

Ruilong Li (@ruilong_li) 's Twitter Profile Photo

For everyone interested in precise 📷camera control 📷 in transformers [e.g., video / world model etc] Stop settling for Plücker raymaps -- use camera-aware relative PE in your attention layers, like RoPE (for LLMs) but for cameras! Paper & code: liruilong.cn/prope/

For everyone interested in precise 📷camera control 📷 in transformers [e.g., video / world model etc]

Stop settling for Plücker raymaps -- use camera-aware relative PE in your attention layers, like RoPE (for LLMs) but for cameras! 

Paper & code: liruilong.cn/prope/
David McAllister (@davidrmcall) 's Twitter Profile Photo

Excited to share Flow Matching Policy Gradients: expressive RL policies trained from rewards using flow matching. It’s an easy, drop-in replacement for Gaussian PPO on control tasks.

Alexandre Morgand (@almorgand) 's Twitter Profile Photo

"Cameras as Relative Positional Encoding" TLDR: comparison for conditioning transformers on cameras: token-level raymap, attention-level relative pose encodings, a (new) relative encoding Projective Positional Encoding -> camera frustums, (int|ext)insics for relative pos encoding

Ruilong Li (@ruilong_li) 's Twitter Profile Photo

Genie3 is like magic! Curious the best way to add viewpoint conditioning signal into transformer? Check this out 👉 liruilong.cn/prope/