A summary of our recent research on efficient diffusion models for images, videos and 3D:
https://t.co/HRqoPVFasB
arXivs:
- https://t.co/TcKm9Khv0i
- https://t.co/0EFdC6dP1U
- https://t.co/kjHVaHtx6i
- https://t.co/nXKy6NHsE5
#GenerativeAI#diffusion#efficient
"HMD-NeMo: Online 3D Avatar Motion Generation from Sparse Observations" will be presented at #ICCV2023. Please come and meet us (Room "Foyer Sud" - 013, on Thursday 10:30AM)
Link to paper: https://t.co/oxy2gEXeem
Together with @fatemeh_saleh, David, Pashmina, and @dopomoc
@cs_jiaxi_jiang@fatemeh_saleh@dopomoc That's a good question, while I don't have enough experiment to back it up, but computing the additional loss on global joint transformation matrices typically boosts the performance a lot. Euc distance for position and geodesic loos for rot mat (in 6D) is very effective
@cs_jiaxi_jiang@fatemeh_saleh@dopomoc 2/n in addition to that (2) our SE(3) loss is contributing a lot when it comes to MPJPE. See fig (9) that removing this loss term leads to a model with MPJPE of >5cm. Finally (3) optimization on top of NeMo's output (table 6/7) yields further improvement.
* 3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces
@jujihong@ukrdailo
The following gives a short summary of these two papers:
https://t.co/ASeL0WIvhs
Thrilled to announce that I will be presenting my previous internship paper at #ICCV2023 in Paris next Thursday! Most work done during an internship at Samsung AI Cambridge. Work w/ @ESanchezLozano, @AdrianBulat, @victorturrisi, @cgmsnoek, @tzimiro, Brais Martinez.
1/2
Check out Imitator, a novel approach to generating personalised speech-driven facial animations.
Come to our #ICCV2023 poster on Friday (Room "Nord" - 058) 😊
Next week @ICCVConference, @balathambiraja will present his work Imitator which learns personalized speech-driven 3D facial animation.
Web : https://t.co/xtDdlTi9eP
Code: https://t.co/0SscNZplOH
In collaboration with Ikhsanul Habibie, @aa_sadegh, @dopomoc, Christian Theobalt