Pixal3D: Turning One Image Into a GLB Asset Draft (Ten Subject Tests)

Image-to-3D has been around long enough that the interesting question is no longer "can it" but "what is it good for". This episode's answer, after ten subjects: Pixal3D is a high-fidelity draft machine for props, fantasy assets and scene objects, not a modeler replacement. Its distinguishing feature is pixel alignment - pixel features from the image are back-projected into 3D so the 2D-to-3D correspondence is tighter. This episode shipped no workflow JSON and no mirror, so the notes below are the author's test results, reported in the video. Evidence note: no workflow JSON and no workflow/mirror link were included with this episode - only the YouTube and Bilibili videos. Everything below is the author's reported testing; no graph parameters exist to quote. When to use it You need a fast blockout of a prop, a game item or a fantasy object. You want a GLB you can open in local 3D software and iterate on. You are building a composite AI + 3D + video pipeline and need asset drafts, not final topology. What is different in this workflow Pixel alignment is the pitch: rather than guessing geometry from semantics, the model back-projects image pixels into 3D space, which tightens the 2D/3D correspondence. Output is GLB, usable outside the tool. It is a draft generator: the author's repeated framing is "3D blank/rough", good for blockout, not for rigging. Step by step Pick a subject with a normal, roughly symmetric viewing angle - the author used stock assets and notes that odd angles hurt results. Run the image through Pixal3D (the author points to a one-click package linked from his Bilibili description; the RunningHub side also has a ComfyUI plugin for this kind of conversion). Open the GLB in your 3D software and inspect silhouette, material separation and back side. Keep it as a blockout; retopologize before rigging. Parameter basis No workflow JSON was shipped and no step/CFG/sampler values were published, so none are quoted here. The only quantitative content is the ten-subject test set: character, metal coffee pot, bicycle, leather chair, robot, glass perfume bottle, backpack, treasure chest, plant, guitar. Limits Character: overall silhouette, clothing layers, cape, boots and arm proportions survive; face, hands and clothing detail get simplified; topology is very unlikely to be riggable. Bicycle: thin structures (rims, spokes, chain, frame crossings) come out as a rough blank - not a usable mechanical model. Robot / guitar: appearance and colour read correctly, fine mechanical detail merges. Glass bottle: looks convincing, but transparency in the GLB is likely texture and surface effect, not physically accurate glass. Input angle matters: the author recommends normal, roughly symmetric views. It is a draft, not a replacement for a modeler. Episode resources YouTube: https://www.youtube.com/watch?v=DTH7jR9bHZ8 Bilibili: https://www.bilibili.com/video/BV1siGb6sEfq Note: no workflow JSON, no RunningHub workflow link and no mirror were shipped with this episode.