Alternatives
Products that do what Neural4D does
Generate production-ready 3D models from images and text.
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Use generative AI to create future-facing videos
2022 · studio.d-id.com
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I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
Apr 2026 · github.com
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It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)
Jun 2026 · github.com
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Hey HN! I’m Zach one of the co-founders of Adam (https://www.adamcad.com). We're building AI-powered tools for CAD and 3D modeling [1]. We’ve recently been exploring a new way to bring GPT-style image editing directly into 3D model generation and are excited to showcase this in our web-app today. We’re calling it creative mode and are intrigued by the fun use cases this could create by making 3D generation more conversational! For example you can put a prompt in such as “an elephant” then follow it up by “have it ride a skateboard” and it preserves the context, identity and…
2025 · adamcad.com
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2018 · github.com
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