81 citations · 109 across the 13 of their papers we have counts for
14 papers
SyncVid: World-Equivariant Diffusion for Synchronized Video Generation
Farhad G. Zanjani, Animesh Karnewar, Fatih Porikli +1
Generating multiple videos of a dynamic scene from a single reference video requires synthesizing both observed and unobserved content consistently across viewpoints. Existing came…
SQuad: Sub-Quadratic Attention Distillation for Efficient Video Generation
Animesh Karnewar, Denis Korzhenkov, Amirhossein Habibian +1
Video Diffusion Transformers (DiTs) spend most of their compute inside the Self-Attention operation, whose cost grows quadratically, , with the number of latent t…
MobileWan: Closing the Quality Gap for Mobile Video Diffusion
Mohsen Ghafoorian, Denis Korzhenkov, Adil Karjauv +9
Recent advances in video diffusion have been driven by scaling transformer-based architectures to billions of parameters, substantially improving visual fidelity and motion coheren…
Track the Noise, Move the World:3D-Grounded Motion-Consistent Noise for Controllable Video Generation
Long Vu, Tan Ngo, Animesh Karnewar +5
Modern image-and-text-to-video diffusion models can synthesize highly realistic videos by iteratively denoising an initial Gaussian noise tensor conditioned on reference image and…
PyramidalWan: On Making Pretrained Video Model Pyramidal for Efficient Inference
Denis Korzhenkov, Adil Karjauv, Animesh Karnewar +2
Recently proposed pyramidal models decompose the conventional forward and backward diffusion processes into multiple stages operating at varying resolutions. These models handle in…
Neodragon: Mobile Video Generation using Diffusion Transformer
Animesh Karnewar, Denis Korzhenkov, Ioannis Lelekas +10
We introduce Neodragon, a text-to-video system capable of generating 2s (49 frames @24 fps) videos at the 640x1024 resolution directly on a Qualcomm Hexagon NPU in a record 6.7s (7…
Meta 3D Gen
Raphael Bensadoun, Tom Monnier, Yanir Kleiman +17
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3…
GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning
Animesh Karnewar, Roman Shapovalov, Tom Monnier +3
Encoding information from 2D views of an object into a 3D representation is crucial for generalized 3D feature extraction. Such features can then enable 3D reconstruction, 3D gener…
HoloFusion: Towards Photo-realistic 3D Generative Modeling
Animesh Karnewar, Niloy J. Mitra, Andrea Vedaldi +1
Diffusion-based image generators can now produce high-quality and diverse samples, but their success has yet to fully translate to 3D generation: existing diffusion methods can eit…
HoloDiffusion: Training a 3D Diffusion Model using 2D Images
Animesh Karnewar, Andrea Vedaldi, David Novotny +1
Diffusion models have emerged as the best approach for generative modeling of 2D images. Part of their success is due to the possibility of training them on millions if not billion…
3inGAN: Learning a 3D Generative Model from Images of a Self-similar Scene
Animesh Karnewar, Oliver Wang, Tobias Ritschel +1
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D "remixes" of a given scen…
ReLU Fields: The Little Non-linearity That Could
Animesh Karnewar, Tobias Ritschel, Oliver Wang +1
In many recent works, multi-layer perceptions (MLPs) have been shown to be suitable for modeling complex spatially-varying functions including images and 3D scenes. Although the ML…
RGBD-Net: Predicting color and depth images for novel views synthesis
Phong Nguyen-Ha, Animesh Karnewar, Lam Huynh +3
We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware g…
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
Animesh Karnewar, Oliver Wang
While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instab…