Bibliographic record
Abstract
3-D tools have been successfully established in several areas of the footwear design process. Yet, 3-D tools often find little adoption during initial concept creation for various reasons. These tools are often slow, difficult to use and limit creativity in ways unacceptable to most designers. The lack of 3-D content creation in the beginning makes it inherently difficult to implement ideal production pipelines that enrich and reuse assets during all steps of the content creation. At adidas, we have successfully established a simple, sketch based 3-D tool which feeds into our 3D design pipeline and finds astonishing acceptance within the design community. Our team presented a digital 3-D footwear design process and pipeline at Siggraph 2017 [Suessmuth et al. 2017]. Tape, the first tool in this pipeline, allows our designers to create meaningful 3-D assets in the early stages of design. In this talk, we explain the origin of Tape and walk the audience through all key features, their purpose in terms of footwear design and their implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".