Portfolio item number 1
Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
Short description of portfolio item number 2
publications
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection
Published in ICCV 2021, 2020
We propose a semi-supervised scheme that forces predicted triplets to be grounded consistently back to the image, addressing context bias in Scene Graph Generators through spatial common sense distillation.
MotionFix: Text-Driven 3D Human Motion Editing
Published in SIGGRAPH Asia 2024, 2024
The MotionFix dataset is the first benchmark for 3D human motion editing from text. It contains triplets of source and target motions, and edit texts that describe the desired modification. Our dataset allows both training and evaluation of models for text-based motion editing. TMED is a conditional diffusion model trained on MotionFix to perform motion editing using both the source motion and the edit text.
WANDR: Goal-Reaching Human Motion Generation
Published in CVPR 2024, 2024
WANDR is a conditional Variational AutoEncoder (c-VAE) that generates realistic motion of human avatars that navigate towards an arbitrary goal location and reach for it. Input to our method is the initial pose of the avatar, the goal location, and the desired motion duration. Output is a sequence of poses that guide the avatar from the initial pose to the goal location and place the wrist on it.
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models
Published in arXiv 2025, 2025
NIL introduces a data-independent approach for motor skill acquisition that learns 3D motor skills from 2D-generated videos, with generalization capability to unconventional and non-human forms. We guide the imitation learning process by leveraging vision transformers for video-based comparisons and demonstrate that NIL outperforms baselines trained on 3D motion-capture data in humanoid robot locomotion tasks.
Moving by Looking: Towards Vision-Driven Avatar Motion Generation
Published in arXiv 2025, 2025
Human-like motion requires human-like perception. We create a human motion generation system is purely driven by Vision.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Stochastics and Machine Learning
Undergraduate course, ETH Zurich, Department of Computer Science, 2025
Teaching Assistant for Stochastics and Machine Learning (252-0870-00L) at ETH Zurich during Spring 2025 semester.