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Modeling human behaviors in contextual environments has a wide range of applications in character animation, embodied AI, VR/AR, and robotics. In real-world scenarios, humans frequently interact with the environment and manipulate various objects to complete daily tasks. In this work, we study the problem of full-body human motion synthesis for the manipulation of large-sized objects. We propose Object MOtion guided human MOtion synthesis (OMOMO), a conditional diffusion framework that can generate full-body manipulation behaviors from only the object motion. Since naively applying diffusion models fails to precisely enforce contact constraints between the hands and the object, OMOMO learns two separate denoising processes to first predict hand positions from object motion and subsequently synthesize full-body poses based on the predicted hand positions. By employing the hand positions as an intermediate representation between the two denoising processes, we can explicitly enforce contact constraints, resulting in more physically plausible manipulation motions. With the learned model, we develop a novel system that captures full-body human manipulation motions by simply attaching a smartphone to the object being manipulated. Through extensive experiments, we demonstrate the effectiveness of our proposed pipeline and its ability to generalize to unseen objects. Additionally, as high-quality human-object interaction datasets are scarce, we collect a large-scale dataset consisting of 3D object geometry, object motion, and human motion. Our dataset contains human-object interaction motion for 15 objects, with a total duration of approximately 10 hours.
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Use It-No Need to Shake It!: Accurate Implicit Authentication for Everyday Objects with Smart Sensing
Implicit authentication for traditional objects, such as doors and dumbbells, has rich applications but is rarely studied. An ongoing trend is that traditional objects are retrofitted to smart environments; for instance, a contact sensor is attached to a door to detect door opening (but cannot tell "who is opening the door"). We present the first accurate implicit-authentication system for retrofitted everyday objects, named MoMatch. It makes an authentication decision based on a single natural object use, unlike prior work that requires shaking objects. MoMatch is built on the observation that an object has a motion typically because a human hand moves it; thus, the object's motion and the legitimate user's hand movement should correlate. The main challenge is, given the small amount of data collected during one object use, how to measure the correlation accurately. We convert the correlation measurement problem into an image comparison problem and resolve it using neural networks successfully. MoMatch does not need to profile the user's biometric information and is resilient to mimicry attacks.
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- PAR ID:
- 10386168
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 6
- Issue:
- 3
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3550322
