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Found 6 papers, 3 papers with code
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
Ranked #4 on
Point Cloud Generation
on ShapeNet Car
Learning Gradient Fields for Shape Generation
Point cloud generation thus amounts to moving randomly sampled points to high-density areas.
DualSDF: Semantic Shape Manipulation using a Two-Level Representation
We are seeing a Cambrian explosion of 3D shape representations for use in machine learning.
Controllable Video Generation With Sparse Trajectories
Video generation and manipulation is an important yet challenging task in computer vision.
GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds
We represent the world as a continuous volumetric function and train our model to render view-consistent photorealistic images for a user-controlled camera.
