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DTSTAMP:20260417T190158Z
LOCATION:West Building\, Ballroom C
DTSTART;TZID=America/Los_Angeles:20250814T111500
DTEND;TZID=America/Los_Angeles:20250814T121500
UID:siggraph_SIGGRAPH 2025_sess186_pros_126@linklings.com
SUMMARY:Time-Traveling VFX: AI-Driven De-Aging in Here
DESCRIPTION:Kevin Baillie (Here Production) and Jo Plaete (Metaphysic AI)\
 n\nJoin Production Supervisor Kevin Baillie and Metaphysic VFX Supervisor 
 Jo Plaete for a deep dive into how artist-empowering artificial intelligen
 ce enabled unprecedented workflows on Robert Zemeckis’s new film, Here. To
 ld from a single perspective that transcends time, Here follows characters
  played by Tom Hanks, Robin Wright, Paul Bettany, and Kelly Reilly across 
 multiple decades of their lives. While these monumental age spans were cru
 cial to the narrative, the production faced an immense technical challenge
 : how to maintain the actors’ performances and emotional realism while rad
 ically altering their appearances.\n\nBaillie and Plaete will explain how 
 the project began with a competitive screen test. At first, traditional 3D
  and motion-capture methods were deemed impractical for the required volum
 e of shots and the nuance demanded by the close-up facial performances. Me
 taphysic’s early proof of concept, transforming the 67-year-old Tom Hanks 
 into his Big-era twenties, demonstrated that an AI-based approach could br
 idge massive age gaps without sacrificing the authenticity of the actors’ 
 expressions. Once the filmmakers chose this route, the Metaphysic team rap
 idly scaled, bringing together AI engineers, data scientists, VFX artists,
  and compositors who refined the technology into a production-ready pipeli
 ne that centered on the principle of empowering filmmakers and artists.\n\
 nA central aspect of this pipeline was real-time on-set face swapping, in 
 which a specialized server equipped with powerful GPUs received a direct f
 eed from the main camera, processed it through neural networks, and return
 ed a de-aged image to the director’s monitor. This setup gave Robert Zemec
 kis and the actors near-instant feedback, only a few frames of delay, allo
 wing them to adjust on the spot. Tom Hanks and Robin Wright rehearsed in f
 ront of a “Youth Mirror” system that let them see their younger faces in r
 eal time, helping them modulate posture, eye lines, and subtle expressions
 . Baillie will recount how the production team integrated these tools into
  daily shooting schedules, and Plaete will offer insights into the technic
 al hurdles of achieving high-fidelity performance transfer on set, highlig
 hting how the AI’s flexibility liberated artists to iterate and refine wit
 h minimal technical friction.\n\nThe session also explores how the technol
 ogy matured in post-production, where higher resolutions and additional re
 finements were required for final shots. By working with “plate prep” and 
 proprietary compositing workflows, the VFX team preserved the liveliness o
 f each performance, even when rewinding several decades, while avoiding th
 e “uncanny valley.” Attendees will learn how neural networks were trained 
 on massive libraries of archival footage for Tom Hanks, Robin Wright, and 
 other cast members, capturing the shifts in bone structure and skin qualit
 y across each stage of life. Plaete will describe the “visual data science
 ” approach his team used to tune these models, emphasizing that successful
  outputs demanded not just code but also a keen artistic and intuitive und
 erstanding of the actors’ faces, underscoring how human creativity remains
  paramount when wielding artist-empowering AI.\n\nThroughout the session, 
 both speakers will emphasize that while neural networks are powerful, they
  function best as a tool for artists.\n\nInterest Area: New Technologies, 
 Production & Animation\n\nRecording: Livestreamed, Recorded\n\nKeyword: Ar
 tificial Intelligence/Machine Learning, Generative AI\n\nRegistration Cate
 gory: Full Conference, Virtual Access, Thursday\n\n
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