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Found 12 papers, 3 papers with code
MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challenge
The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) is the second annual CVPR workshop aimed at the monitoring and analysis of the health of Earth ecosystems by leveraging the vast amount of remote sensing data that is continuously being collected.
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge
The Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022) will be the first competition aimed at the monitoring and analysis of deforestation in the Amazon rainforest at any time and in any weather conditions.
Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows
Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervised learning in complex visual scenes.
Ranked #53 on
3D Human Pose Estimation
on 3DPW
(PA-MPJPE metric)
Visual Object Networks: Image Generation with Disentangled 3D Representations
The VON not only generates images that are more realistic than the state-of-the-art 2D image synthesis methods but also enables many 3D operations such as changing the viewpoint of a generated image, shape and texture editing, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.
Learning to Exploit Stability for 3D Scene Parsing
We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples.
Learning to See Physics via Visual De-animation
At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by physics and graphics engines.
Shape and Material from Sound
Hearing an object falling onto the ground, humans can recover rich information including its rough shape, material, and falling height.
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
Humans demonstrate remarkable abilities to predict physical events in dynamic scenes, and to infer the physical properties of objects from static images.
Shape and Illumination from Shading using the Generic Viewpoint Assumption
The Generic Viewpoint Assumption (GVA) states that the position of the viewer or the light in a scene is not special.
Nonparametric Bayesian Texture Learning and Synthesis
2D-HMM is coupled with the Hierarchical Dirichlet process (HDP) which allows the number of textons and the complexity of transition matrix grow as the input texture becomes irregular.
Segmenting Scenes by Matching Image Composites
In contrast to recent work in semantic alignment of scenes, we allow an input image to be explained by partial matches of similar scenes.
