1 citations · 2 across the 5 of their papers we have counts for
6 papers
Learning to Read the Contextual Tokens in Diffusion Transformers
Omer Dahary, Etai Sella, Hadar Averbuch-Elor +2
Multimodal Diffusion Transformers (MM-DiTs) jointly process visual and textual representations throughout generation. These models repeatedly update the text tokens through multimo…
Prox-E: Fine-Grained 3D Shape Editing via Primitive-Based Abstractions
Etai Sella, Hao Phung, Nitay Amiel +3
Text-based 2D image editing models have recently reached an impressive level of maturity, motivating a growing body of work that heavily depends on these models to drive 3D edits.…
LooseRoPE: Content-aware Attention Manipulation for Semantic Harmonization
Etai Sella, Yoav Baron, Hadar Averbuch-Elor +2
Recent diffusion-based image editing methods commonly rely on text or high-level instructions to guide the generation process, offering intuitive but coarse control. In contrast, w…
InstanceGen: Image Generation with Instance-level Instructions
Etai Sella, Yanir Kleiman, Hadar Averbuch-Elor
Despite rapid advancements in the capabilities of generative models, pretrained text-to-image models still struggle in capturing the semantics conveyed by complex prompts that comp…
Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention
Etai Sella, Gal Fiebelman, Noam Atia +1
We are witnessing rapid progress in automatically generating and manipulating 3D assets due to the availability of pretrained text-image diffusion models. However, time-consuming o…
Vox-E: Text-guided Voxel Editing of 3D Objects
Etai Sella, Gal Fiebelman, Peter Hedman +1
Large scale text-guided diffusion models have garnered significant attention due to their ability to synthesize diverse images that convey complex visual concepts. This generative…