Reconstruct a moving, deforming object in 4D (3D + time) from a single-camera video — even under heavy occlusion and large non-rigid motion 🎥 Title: Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild URL: 🎥 Overview A test-time optimization framework combining single-view 3D prediction with deformable 3D Gaussian Splatting. It fuses learned geometry/appearance priors with the video to handle severe occlusion and non-rigid motion. ❓ Problem it solves ・Directly predicting 4D is limited by scarce training data and generalizes poorly ・Initializing 3D and refining with video alone breaks down on in-the-wild scenes with large deformation and occlusion 💡 Methodology Three components: ・Causal Latent Conditioning: aligns an image-to-3D model temporally without retraining, coupling adjacent latents at the ODE level, with t₀ trading off consistency vs per-frame fidelity ・Deformable Gaussians: sparse control nodes with time-varying SE(3) transforms + linear blend skinning deform a canonical representation across frames ・Occlusion-aware refinement: compares only visible pixels, while a view-conditioned diffusion prior completes unobserved surfaces 📊 Results ・Compared against STAG4D, PAD3R, L4GM, DreamMesh4D, V2M4, BANMo ・On Pexels data, point-tracking error EPE 0.072 (vs 0.119–0.211 for competitors) ・~30 min per 32-frame video on a single H200 GPU #3DReconstruction# #GaussianSplatting#

🪑 Insert an object into an image while specifying its exact 3D orientation and position. DIRECT solves the 3D-pose control that text leaves ambiguous and parameters struggle with, by decomposing visual proxies. Title: Direct 3D-Aware Object Insertion via Decomposed Visual Proxies URL: 📝 Overview DIRECT is a diffusion-based method that inserts a reference object into an image with explicit control over its 3D pose and position. It decomposes the insertion condition into geometry, appearance, and context, injected through independent pathways. ❓ Challenges Solved Existing insertion methods formulate the task as 2D inpainting and can't control 3D pose. Text guidance is spatially ambiguous, and parametric 3D methods can't translate abstract parameters into correct geometric projections. 💡 Methodology & Proposed Approach ・A user-manipulated 3D proxy rendered at the target pose provides geometry guidance ・Appearance (the reference's high-fidelity look) and context (background semantics) are injected independently via separate LoRA adapters and positional embeddings to avoid feature entanglement ・TRELLIS lifts the image into a coarse 3D shape, refined with VGGT and 3D Gaussian Splatting ・Built on FLUX.1-Fill, it uses shape-decomposed mask augmentation and progressive-resolution training to avoid overfitting 🎯 Use Cases It fits virtual staging, e-commerce product photography, creative work needing precise spatial control, and photorealistic AR/VR content generation. 📊 Experimental Results ・On the FLUX backbone it reaches PSNR 23.09, LPIPS 0.147, and matching error 17.8, beating baselines on all metrics ・It stays stable across large 0-180 degree pose changes and preserves fine details even under 3D-reconstruction degradation ・Hybrid-data training raised CLIP-I from 0.904 to 0.943 ・For symmetric object orientation, RGB geometry guidance outperformed normal maps #3DGeneration# #ImageEditing#

The future of football replay is more immersive ⚽ Lenovo AI-powered 3D Digital Avatars use advanced 3D reconstruction for ultra-realistic player views at FIFA World Cup 2026™.

That grainy tangle of colors is real brain tissue, mapped in more detail than any human brain sample ever has been. The sample came from a woman's temporal lobe, removed during surgery to treat her epilepsy, and donated for research afterward. A team from Google Research and Jeff Lichtman's lab at Harvard sliced it into roughly 5,000 wafer-thin sections, imaged each one with an electron microscope, then used machine learning to digitally stitch everything back into one continuous 3D reconstruction. No human could trace this by hand, the resulting dataset alone runs to 1.4 petabytes, more storage than most people will use in a lifetime. Inside that single cubic millimeter, roughly one millionth of an entire human brain, the team counted about 57,000 individual cells, nearly 150 million synapses connecting them, and 230 millimeters of blood vessels threading through the tissue. The colors in images like this one aren't decorative, they're how researchers visually separate individual neurons and cell types from the tangle around them so they can trace each one's path. The map turned up some genuine surprises too. Most neuron pairs connect through just a couple of synapses, but researchers found rare pairs linked by up to 50 connections at once. They also spotted neurons whose branches curl around and knot into themselves, something nobody had documented before, along with pairs of neurons that turned out to be near perfect mirror images of each other. Knowing this level of detail exists for just one millionth of a brain, how far off do you think a full human connectome actually is?

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