Papersby GlyphLoST: Level of Semantics Tokenization for 3D Shapes research paper by Adobe, 2026
Adobe · Multimodal and robotics · Mar 18, 2026 · 4 citations · 32 upvotes · unverified
What it shows
Level-of-Semantics Tokenization (LoST) improves 3D shape generation by ordering tokens based on semantic salience and using a novel relational alignment loss for better reconstruction and efficiency.
By Niladri Shekhar Dutt, Zifan Shi, Paul Guerrero and 4 more · arXiv 2603.17995 · PDF · Code
UnverifiedHugging Face's summary; not yet checked by hand.
More from Adobe
All 9MoviEdit: Long-form Multi-Shot Video Editing Made EasyMoviEdit is a framework and benchmark for editing long videos across multiple camera shots and changing scenes.
Adobe75 upvotesRead →Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic DesignDesigner-RSI improves agentic graphic design by continually refining a procedural memory of skills based on user traffic and execution feedback.Adobe1 citations34 upvotesRead →Beyond Starry Night: Shortcut-Aware Control-State Planning for Artist-Grounded Text to Image GenerationAtelier improves artist-grounded image generation by translating vague artistic intent into explicit control states that separate scene content from style, reducing reliance on stereotypical shortcuts.Adobe1 citations28 upvotesRead →WorldCam: Interactive Autoregressive 3D Gaming Worlds with Camera Pose as a Unifying Geometric RepresentationVideo diffusion transformers enhanced with camera pose representation enable precise action control and long-term 3D consistency in interactive gaming environments through physics-based action spaces and geometric grounding.Adobe14 citations61 upvotesRead →Memory-V2V: Augmenting Video-to-Video Diffusion Models with MemoryMemory-V2V enhances multi-turn video editing by maintaining cross-consistency through explicit memory mechanisms and efficient token compression in video-to-video diffusion models.Adobe3 citations28 upvotesRead →Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and EditingLatent diffusion models using representation encoder features face challenges in semantic compactness and pixel-level reconstruction, which are addressed through a semantic-pixel reconstruction objective that enables compact yet semantically rich representations for unified text-to-image and image editing tasks.Adobe28 citations37 upvotesRead →V-RGBX: Video Editing with Accurate Controls over Intrinsic PropertiesV-RGBX presents an end-to-end framework for intrinsic-aware video editing that combines video inverse rendering, photorealistic video synthesis, and keyframe-based editing with physically grounded intrinsic channel manipulation.Adobe7 citations30 upvotesRead →MotionStream: Real-Time Video Generation with Interactive Motion ControlsMotionStream enables real-time video generation with sub-second latency and up to 29 FPS by distilling a text-to-video model with motion control into a causal student using Self Forcing with Distribution Matching Distillation and sliding-window causal attention with attention sinks.Adobe88 citations33 upvotesRead →
Other multimodal and robotics papers
TopicSegment AnythingA promptable model and a dataset of over a billion masks that cut out any object in any image.
Meta16.8k citations6 upvotesRead →BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsBLIP-2 efficiently pre-trains vision-language models using off-the-shelf frozen encoders and decoders, achieving state-of-the-art performance with fewer parameters.
Salesforce9,805 citations3 upvotesRead →BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationBLIP, a Vision-Language Pre-training framework, improves performance across both understanding and generation tasks by bootstrapping captions from noisy web data, achieving state-of-the-art results on image-text retrieval, image captioning, and VQA, and showing strong generalization to video-language tasks.Salesforce7,655 citations5 upvotesRead →GPT-4o System CardGPT-4o is an omnimodal autoregressive model trained to handle text, audio, image, and video inputs, offering high-performance outputs across these modalities, with particular strengths in vision and audio.
OpenAI5,388 citations88 upvotesRead →SAM 3: Segment Anything with ConceptsSegment Anything Model 3 achieves state-of-the-art performance in promptable concept segmentation and tracking by leveraging a unified model architecture with decoupled recognition and localization.Meta1,466 citations138 upvotesRead →DeepSeek-VL: Towards Real-World Vision-Language UnderstandingDeepSeek-VL is an open-source vision-language model that achieves state-of-the-art performance in real-world applications by combining a hybrid vision encoder with effective pretraining strategies to preserve language model capabilities.
DeepSeek939 citations49 upvotesRead →
About this paper
- Venue
- Computer Vision and Pattern Recognition
- Citations
- 4, 2 influential · Semantic Scholar
- Upvotes
- 32 · Hugging Face
- Lab
- Adobe · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf
Changes
Citations rose from 3 to 4.
A first count of 2 influential citations.
A first count of 3 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.