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Found 20 papers, 8 papers with code
Birth and Death of a Rose
We study the problem of generating temporal object intrinsics -- temporally evolving sequences of object geometry, reflectance, and texture, such as a blooming rose -- from pre-trained 2D foundation models.
Diffusion Self-Distillation for Zero-Shot Customized Image Generation
We first leverage a text-to-image diffusion model's in-context generation ability to create grids of images and curate a large paired dataset with the help of a Visual-Language Model.
The Scene Language: Representing Scenes with Programs, Words, and Embeddings
We introduce the Scene Language, a visual scene representation that concisely and precisely describes the structure, semantics, and identity of visual scenes.
3D Congealing: 3D-Aware Image Alignment in the Wild
The framework optimizes for the canonical representation together with the pose for each input image, and a per-image coordinate map that warps 2D pixel coordinates to the 3D canonical frame to account for the shape matching.
SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background.
Learning the 3D Fauna of the Web
We show that prior category-specific attempts fail to generalize to rare species with limited training images.
Ponymation: Learning 3D Animal Motions from Unlabeled Online Videos
We introduce Ponymation, a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos.
Language-Informed Visual Concept Learning
To encourage better disentanglement of different concept encoders, we anchor the concept embeddings to a set of text embeddings obtained from a pre-trained Visual Question Answering (VQA) model.
Holistic Evaluation of Text-To-Image Models
The stunning qualitative improvement of recent text-to-image models has led to their widespread attention and adoption.
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image
Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views.
Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark.
IKEA-Manual: Seeing Shape Assembly Step by Step
Human-designed visual manuals are crucial components in shape assembly activities.
Seeing a Rose in Five Thousand Ways
These instances all share the same intrinsics, but appear different due to a combination of variance within these intrinsics and differences in extrinsic factors, such as pose and illumination.
Translating a Visual LEGO Manual to a Machine-Executable Plan
We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions.
MaskViT: Masked Visual Pre-Training for Video Prediction
This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling.
Video Extrapolation in Space and Time
Novel view synthesis (NVS) and video prediction (VP) are typically considered disjoint tasks in computer vision.
VideoGPT: Video Generation using VQ-VAE and Transformers
We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.
VideoGen: Generative Modeling of Videos using VQ-VAE and Transformers
We present VideoGen: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.
Automatic Curriculum Learning through Value Disagreement
Our key insight is that if we can sample goals at the frontier of the set of goals that an agent is able to reach, it will provide a significantly stronger learning signal compared to randomly sampled goals.
Asynchronous Methods for Model-Based Reinforcement Learning
In this work, we propose an asynchronous framework for model-based reinforcement learning methods that brings down the run time of these algorithms to be just the data collection time.
Model-based Reinforcement Learning
reinforcement-learning
+2
