ReConText3D is the first replay-memory framework for continual text-to-3D generation that prevents catastrophic forgetting on new textual categories while preserving quality on previously seen classes.
A survey on text-to-3d contents generation in the wild
4 Pith papers cite this work. Polarity classification is still indexing.
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Introduces 3D-CBM framework mapping raw 3D inputs to multi-tiered interpretable concepts, achieving 88.8% concept accuracy and test-time intervention on PartNet and ShapeNet.
SpatialPrompt turns spatial sketches and voice prompts into executable constraints for controllable AI 3D generation in XR, enabling iterative collaborative creation with color-coded contributions.
An edge-deployed SLM classifies user prompt intent to route requests to heterogeneous AI backends in virtual worlds, with evaluation in a museum testbed showing feasibility of fine-tuned sub-billion models for low-latency orchestration.
citing papers explorer
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ReConText3D: Replay-based Continual Text-to-3D Generation
ReConText3D is the first replay-memory framework for continual text-to-3D generation that prevents catastrophic forgetting on new textual categories while preserving quality on previously seen classes.
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3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling
Introduces 3D-CBM framework mapping raw 3D inputs to multi-tiered interpretable concepts, achieving 88.8% concept accuracy and test-time intervention on PartNet and ShapeNet.
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SpatialPrompt: XR-Based Spatial Intent Expression as Executable Constraints for AI Generative 3D Design
SpatialPrompt turns spatial sketches and voice prompts into executable constraints for controllable AI 3D generation in XR, enabling iterative collaborative creation with color-coded contributions.
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From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds
An edge-deployed SLM classifies user prompt intent to route requests to heterogeneous AI backends in virtual worlds, with evaluation in a museum testbed showing feasibility of fine-tuned sub-billion models for low-latency orchestration.