104 citations · 302 across the 45 of their papers we have counts for
53 papers
SemCam: Semantic Camera Motion Control for Video Generation
Janna Bruner, Omer Talmi, Ianir Ideses +3
Controlling the camera relative to a moving subject in an existing video is challenging: behaviors such as maintaining a frontal view require the camera to adapt to the subject's c…
MV-Forcing: Long Multi-View Video Generation via 4D-Grounded Spatio-Temporal Self-Forcing
Gal Fiebelman, Hadar Averbuch-Elor, Sagie Benaim
Recent advances in video diffusion models have enabled either long single-view generation through temporal autoregression, or short multi-view synthesis through bidirectional atten…
MACRO: Training-free Multi-plane Attention for Closeup Render Optimization
Nitzan Hodos, Roy Amoyal, Lior Fritz +3
Close-up rendering, zooming into a scene well beyond any training camera, is important for virtual production and interactive 3D content, yet remains an open challenge. 3D Gaussian…
TrajLoc: Trajectory-Attention Localization for Multi-Object Motion Control
Omer Sela, Inbar Huberman-Spiegelglas, Michael Rotman +2
Controlling the motion of multiple objects in image-to-video (I2V) generation requires preserving object identities while enforcing adherence to distinct target trajectories. This…
SpheRoPE: Zero-Shot Optimization-Free 360 Panorama Generation with Spherical RoPE
Or Hirschorn, Aaron Olender, Eli Alshan +3
We present a zero-shot, training-free and optimization-free framework for generating 360 panoramic images and videos by directly injecting spherical priors into pre-trained diffusi…
Colored Noise Diffusion Sampling
Hadar Davidson, Noam Issachar, Sagie Benaim
Diffusion models achieve state-of-the-art image synthesis, with their generative trajectories fundamentally exhibiting a spectral bias, resolving low-frequency global structures ea…
PhyGenHOI: Physically-Aware 4D Generation of Dynamic Human-Object Interactions
Omer Benishu, Gal Fiebelman, Sagie Benaim
We address the task of generating physically accurate and visually faithful 4D Human-Object Interaction (HOI). Given a static 3D human and target object represented as 3D Gaussian…
GlobalSplat: Efficient Feed-Forward 3D Gaussian Splatting via Global Scene Tokens
Roni Itkin, Noam Issachar, Yehonatan Keypur +3
The efficient spatial allocation of primitives serves as the foundation of 3D Gaussian Splatting, as it directly dictates the synergy between representation compactness, reconstruc…
RAD: Retrieval-Augmented Monocular Metric Depth Estimation for Underrepresented Classes
Michael Baltaxe, Dan Levi, Sagie Benaim
Monocular Metric Depth Estimation (MMDE) is essential for physically intelligent systems, yet accurate depth estimation for underrepresented classes in complex scenes remains a per…
Splat and Distill: Augmenting Teachers with Feed-Forward 3D Reconstruction For 3D-Aware Distillation
David Shavin, Sagie Benaim
Vision Foundation Models (VFMs) have achieved remarkable success when applied to various downstream 2D tasks. Despite their effectiveness, they often exhibit a critical lack of 3D…
SemanticMoments: Training-Free Motion Similarity via Third Moment Features
Saar Huberman, Kfir Goldberg, Or Patashnik +2
Retrieving videos based on semantic motion is a fundamental, yet unsolved, problem. Existing video representation approaches overly rely on static appearance and scene context rath…
Lang3D-XL: Language Embedded 3D Gaussians for Large-scale Scenes
Shai Krakovsky, Gal Fiebelman, Sagie Benaim +1
Embedding a language field in a 3D representation enables richer semantic understanding of spatial environments by linking geometry with descriptive meaning. This allows for a more…
DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion
Noam Issachar, Guy Yariv, Sagie Benaim +3
Diffusion Transformer models can generate images with remarkable fidelity and detail, yet training them at ultra-high resolutions remains extremely costly due to the self-attention…
MV-RAG: Retrieval Augmented Multiview Diffusion
Yosef Dayani, Omer Benishu, Sagie Benaim
Text-to-3D generation approaches have advanced significantly by leveraging pretrained 2D diffusion priors, producing high-quality and 3D-consistent outputs. However, they often fai…
Let it Snow! Animating 3D Gaussian Scenes with Dynamic Weather Effects via Physics-Guided Score Distillation
Gal Fiebelman, Hadar Averbuch-Elor, Sagie Benaim
3D Gaussian Splatting has recently enabled fast and photorealistic reconstruction of static 3D scenes. However, dynamic editing of such scenes remains a significant challenge. We i…
RewardSDS: Aligning Score Distillation via Reward-Weighted Sampling
Itay Chachy, Guy Yariv, Sagie Benaim
Score Distillation Sampling (SDS) has emerged as an effective technique for leveraging 2D diffusion priors for tasks such as text-to-3D generation. While powerful, SDS struggles wi…
Structurally Disentangled Feature Fields Distillation for 3D Understanding and Editing
Yoel Levy, David Shavin, Itai Lang +1
Recent work has demonstrated the ability to leverage or distill pre-trained 2D features obtained using large pre-trained 2D models into 3D features, enabling impressive 3D editing…
Designing a Conditional Prior Distribution for Flow-Based Generative Models
Noam Issachar, Mohammad Salama, Raanan Fattal +1
Flow-based generative models have recently shown impressive performance for conditional generation tasks, such as text-to-image generation. However, current methods transform a gen…
Through-The-Mask: Mask-based Motion Trajectories for Image-to-Video Generation
Guy Yariv, Yuval Kirstain, Amit Zohar +5
We consider the task of Image-to-Video (I2V) generation, which involves transforming static images into realistic video sequences based on a textual description. While recent advan…
Generating Intermediate Representations for Compositional Text-To-Image Generation
Ran Galun, Sagie Benaim
Text-to-image diffusion models have demonstrated an impressive ability to produce high-quality outputs. However, they often struggle to accurately follow fine-grained spatial infor…
Coarse-To-Fine Tensor Trains for Compact Visual Representations
Sebastian Loeschcke, Dan Wang, Christian Leth-Espensen +3
The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstructio…
LaMI: Augmenting Large Language Models via Late Multi-Image Fusion
Guy Yariv, Idan Schwartz, Yossi Adi +1
Commonsense reasoning often requires both textual and visual knowledge, yet Large Language Models (LLMs) trained solely on text lack visual grounding (e.g., "what color is an emper…
DGD: Dynamic 3D Gaussians Distillation
Isaac Labe, Noam Issachar, Itai Lang +1
We tackle the task of learning dynamic 3D semantic radiance fields given a single monocular video as input. Our learned semantic radiance field captures per-point semantics as well…
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen +7
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Alt…