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David Schneider

7 accepted papers

2026

FlowNar: Scalable Streaming Narration for Long-Form Videos

ICML 2026poster

Recent Large Multimodal Models (LMMs), primarily designed for offline settings, are ill-suited for the dynamic requirements of streaming video. While recent online adaptations improve real-time processing, they still face critical scalability challenges, with resource demands typically growing at le…

Cited by 0SourceScholar
2024

Muscles in Time: Learning to Understand Human Motion In-Depth by Simulating Muscle Activations

NeurIPS 2024poster

Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attributed to the intensive resources required for acquiring ground truth muscle activation data, resulting in a scarcity of dat…

Cited by 3SourcePDFScholar
2024

Navigating Open Set Scenarios for Skeleton-Based Action Recognition

AAAI 2024technical

In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones. However, using pure skeleton data in such open-set conditions poses challenges due to the lack of visual background cues and t…

2024

SynthAct: Towards Generalizable Human Action Recognition based on Synthetic Data

ICRA 2024poster

Synthetic data generation is a proven method for augmenting training sets without the need for extensive setups, yet its application in human activity recognition is underexplored. This is particularly crucial for human-robot collaboration in household settings, where data collection is often privac…

Cited by 3SourceScholar
2022

Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily Living

IROS 2022poster

Domain shifts, such as appearance changes, are a key challenge in real-world applications of activity recognition models, which range from assistive robotics and smart homes to driver observation in intelligent vehicles. For example, while simulations are an excellent way of economical data collecti…

Cited by 3SourcecodeScholar
2021

Let’s Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games

IROS 2021poster

Recognizing Activities of Daily Living (ADL) is a vital process for intelligent assistive robots, but collecting large annotated datasets requires time-consuming temporal labeling and raises privacy concerns, e.g., if the data is collected in a real household. In this work, we explore the concept of…

Cited by 49SourcecodeScholar
2019

An Interactive Indoor Drone Assistant

IROS 2019poster

With the rapid advance of sophisticated control algorithms, the capabilities of drones to stabilise, fly and manoeuvre autonomously have dramatically improved, enabling us to pay greater attention to entire missions and the interaction of a drone with humans and with its environment during the cours…

Cited by 14SourceScholar