MemoryDiorama: Generating Dynamic 3D Diorama from Everyday Photos for Memory Recall

Fuente: arXiv
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Autori principali: Ihara, Keiichi, Li, Tianle, Shiino, Yasuhisa, Suzuki, Ryo
Natura: Preprint
Pubblicazione: 2026
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author Ihara, Keiichi
Li, Tianle
Shiino, Yasuhisa
Suzuki, Ryo
author_facet Ihara, Keiichi
Li, Tianle
Shiino, Yasuhisa
Suzuki, Ryo
contents We present MemoryDiorama, a prototype system that introduces augmented memory cues, a concept that extends captured personal media with AI-generated contextual information to enhance autobiographical memory recall. MemoryDiorama transforms everyday photos into dynamic 3D dioramas in mixed reality by integrating LLM-based scene analysis with 3D object generation, animation, and spatial composition. The system extracts geographic information, object attributes, lighting conditions, and atmospheric elements from the photos. It then animates these elements with generative components such as object animations, human motion, geographical effects, and particle effects to provide richer cues for memory recall. We evaluated MemoryDiorama in a within-subject user study with 18 participants, comparing three conditions: Photo-Only, Static Diorama, and MemoryDiorama. Compared with both Photo-Only and Static Diorama, MemoryDiorama elicited more internal and in-cue details during recall. It also increased perceptual details and visual vividness ratings, suggesting richer recollective experience.
format Preprint
id arxiv_https___arxiv_org_abs_2604_06773
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle MemoryDiorama: Generating Dynamic 3D Diorama from Everyday Photos for Memory Recall
Ihara, Keiichi
Li, Tianle
Shiino, Yasuhisa
Suzuki, Ryo
Human-Computer Interaction
We present MemoryDiorama, a prototype system that introduces augmented memory cues, a concept that extends captured personal media with AI-generated contextual information to enhance autobiographical memory recall. MemoryDiorama transforms everyday photos into dynamic 3D dioramas in mixed reality by integrating LLM-based scene analysis with 3D object generation, animation, and spatial composition. The system extracts geographic information, object attributes, lighting conditions, and atmospheric elements from the photos. It then animates these elements with generative components such as object animations, human motion, geographical effects, and particle effects to provide richer cues for memory recall. We evaluated MemoryDiorama in a within-subject user study with 18 participants, comparing three conditions: Photo-Only, Static Diorama, and MemoryDiorama. Compared with both Photo-Only and Static Diorama, MemoryDiorama elicited more internal and in-cue details during recall. It also increased perceptual details and visual vividness ratings, suggesting richer recollective experience.
title MemoryDiorama: Generating Dynamic 3D Diorama from Everyday Photos for Memory Recall
topic Human-Computer Interaction
url https://arxiv.org/abs/2604.06773