10 articles tagged with #3d-modeling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce PERSIST, a new world model paradigm that maintains persistent 3D spatial memory and consistent geometry for interactive video generation. The model addresses limitations of existing approaches by simulating the evolution of latent 3D scenes, enabling more realistic user experiences and supporting novel capabilities like single-image 3D environment synthesis.
UrbanVerse introduces a data-driven system that converts city-tour videos into realistic urban simulation environments for training AI agents like delivery robots. The system includes 100K+ annotated 3D urban assets and shows significant improvements in navigation success rates, with +30.1% better performance in real-world transfers.
Researchers introduce MVHOI, a new AI framework that significantly improves human-object interaction video generation by handling complex 3D manipulations through a two-stage process using 3D foundation models. The system can create realistic long-duration videos showing intricate object manipulations from multiple viewpoints, addressing limitations of existing approaches that struggle with non-planar movements.
FALCON introduces a novel vision-language-action model that bridges the spatial reasoning gap by injecting 3D spatial tokens into action heads while preserving language reasoning capabilities. The system achieves state-of-the-art performance across simulation benchmarks and real-world tasks by leveraging spatial foundation models to provide geometric priors from RGB input alone.
Researchers developed Cryo-SWAN, a new AI autoencoder network that uses wavelet decomposition to better represent 3D molecular structures from cryo-electron microscopy data. The model outperforms existing 3D autoencoders on multiple datasets and can integrate with diffusion models for molecular shape generation and denoising.
Researchers introduce Mamba-CAD, a state space model using Mamba architecture for generating complex 3D CAD models from parametric sequences. The model addresses limitations in handling longer, fine-grained industrial CAD sequences through an encoder-decoder framework paired with GANs, trained on a new dataset of 77,078 CAD models.
Sketch2Colab is a new AI system that converts 2D sketches into realistic 3D multi-human animations with precise control over interactions and movements. The technology uses a novel approach combining sketch-driven diffusion with rectified-flow distillation for faster, more stable animation generation than existing methods.
Researchers introduced Seek-CAD, a new system that uses the open-source DeepSeek-R1 language model to generate 3D CAD models locally without requiring expensive cloud-based AI services. The system incorporates visual feedback and self-refinement mechanisms to improve CAD model generation, potentially making AI-assisted design more accessible for industrial applications.
Researchers introduce Evidential Neural Radiance Fields, a new probabilistic approach that enables uncertainty quantification in 3D scene modeling while maintaining rendering quality. The method addresses critical limitations in existing NeRF technology by capturing both aleatoric and epistemic uncertainty from a single forward pass, making neural radiance fields more suitable for safety-critical applications.
Researchers have developed Lilium, an automated evolutionary method that uses AI to improve skull-face overlay accuracy in forensic identification of skeletal remains. The system employs a Differential Evolution algorithm with 3D cone-based representation to model soft-tissue variability and outperforms existing state-of-the-art methods.
