22 citations · 22 across the 7 of their papers we have counts for
5 papers · 1 filter
Making Video Models Adhere to User Intent with Minor Adjustments
Daniel Ajisafe, Eric Hedlin, Helge Rhodin +1
With the recent drastic advancements in text-to-video diffusion models, controlling their generations has drawn interest. A popular way for control is through bounding boxes or lay…
Unsupervised Keypoints from Pretrained Diffusion Models
Eric Hedlin, Gopal Sharma, Shweta Mahajan +5
Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures, but performance is yet to match the supervised…
Unsupervised Semantic Correspondence Using Stable Diffusion
Eric Hedlin, Gopal Sharma, Shweta Mahajan +4
Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images. To generate such images, these models must understand the sem…
CN-DHF: Compact Neural Double Height-Field Representations of 3D Shapes
Eric Hedlin, Jinfan Yang, Nicholas Vining +2
We introduce CN-DHF (Compact Neural Double-Height-Field), a novel hybrid neural implicit 3D shape representation that is dramatically more compact than the current state of the art…
A Simple Method to Boost Human Pose Estimation Accuracy by Correcting the Joint Regressor for the Human3.6m Dataset
Eric Hedlin, Helge Rhodin, Kwang Moo Yi
Many human pose estimation methods estimate Skinned Multi-Person Linear (SMPL) models and regress the human joints from these SMPL estimates. In this work, we show that the most wi…