Name
Abstract | ||
|---|---|---|
AbstractCurrent Virtual Reality (VR) systems are bereft of stylization and embellishment of the user's motion - concepts that have been well explored in animations for games and movies. We present CooIMoves, a system for expressive and accentuated full-body motion synthesis of a user's virtual avatar in real-time, from the limited input cues afforded by current consumer-grade VR systems, specifically headset and hand positions. We make use of existing motion capture databases as a template motion repository to draw from. We match similar spatio-temporal motions present in the database and then interpolate between them using a weighted distance metric. Joint prediction probability is then used to temporally smooth the synthesized motion, using human motion dynamics as a prior. This allows our system to work well even with very sparse motion databases (e.g., with only 3-5 motions per action). We validate our system with four experiments: a technical evaluation of our quantitative pose reconstruction and three additional user studies to evaluate the motion quality, embodiment and agency. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3463499 | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies |
DocType | Volume | Issue |
Journal | 5 | 2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karan Ahuja | 1 | 28 | 9.13 |
| Eyal Ofek | 2 | 1865 | 106.07 |
| Mar Gonzalez-Franco | 3 | 169 | 20.04 |
| Christian Holz | 4 | 878 | 56.58 |
| Andrew D. Wilson | 5 | 5065 | 362.19 |