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Found 22 papers, 10 papers with code
VideoPanda: Video Panoramic Diffusion with Multi-view Attention
High resolution panoramic video content is paramount for immersive experiences in Virtual Reality, but is non-trivial to collect as it requires specialized equipment and intricate camera setups.
Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning
We begin by defining key capabilities for Physical AI reasoning, with a focus on physical common sense and embodied reasoning.
Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
We introduce Cosmos-Transfer, a conditional world generation model that can generate world simulations based on multiple spatial control inputs of various modalities such as segmentation, depth, and edge.
Cosmos World Foundation Model Platform for Physical AI
In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups.
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model.
Edify 3D: Scalable High-Quality 3D Asset Generation
We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation.
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models
We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy.
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
Despite the impressive progress, research on metrics for evaluating the quality of generated videos, especially concerning temporal and motion consistency, remains underexplored.
L4GM: Large 4D Gaussian Reconstruction Model
We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second.
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation
VFC consists of three steps: 1) proposal, where image-to-text captioning models propose multiple initial captions; 2) verification, where a large language model (LLM) utilizes tools such as object detection and VQA models to fact-check proposed captions; 3) captioning, where an LLM generates the final caption by summarizing caption proposals and the fact check verification results.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt.
XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
We present XCube (abbreviated as $\mathcal{X}^3$), a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes.
ATT3D: Amortized Text-to-3D Object Synthesis
Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields.
Magic3D: High-Resolution Text-to-3D Content Creation
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results.
Ranked #2 on
Text to 3D
on T$^3$Bench
LION: Latent Point Diffusion Models for 3D Shape Generation
To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes.
Ranked #1 on
Point Cloud Generation
on ShapeNet Airplane
Improving Semantic Segmentation in Transformers using Hierarchical Inter-Level Attention
HILA extends hierarchical vision transformer architectures by adding local connections between features of higher and lower levels to the backbone encoder.
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation
1) We propose a non-parametric prior distribution over the appearance of image parts so that the latent variable ``what-to-draw'' per step becomes a categorical random variable.
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy.
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In practice, it performs similarly to the Hungarian algorithm during inference.
Scene Graph Parsing as Dependency Parsing
The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49. 67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%.
Adversarial Attacks Beyond the Image Space
Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.
