Recreating a 70-year love story frame by frame

逐帧重现一段 70 年的爱情故事

“Love, Rendered,” a new documentary short, explores memory loss and the power of storytelling — and it showcases how technology can help rekindle memories that have begun to fade. The film follows Burt and Ethelle Shatz, a couple married for over 70 years, as they navigate Burt’s cognitive decline. Among his fading memories is one of their most precious: the day they met at a student co-op in Cleveland. Because it wasn’t photographed or filmed, the day existed only in their minds.

一部名为《Love, Rendered》的全新纪录短片探讨了记忆丧失与叙事的力量，并展示了科技如何帮助重燃那些逐渐消逝的记忆。影片记录了结婚超过 70 年的 Burt 和 Ethelle Shatz 夫妇，在面对 Burt 认知能力下降时的生活点滴。在他逐渐模糊的记忆中，最珍贵的一段莫过于他们在克利夫兰的学生合作社初次相遇的那一天。由于当时没有留下任何照片或影像，这一天只存在于他们的脑海中。

When I stepped in as the film’s technical lead, I knew the project would be as emotionally demanding as it was technically complex. Memory loss is personal to me. My grandfather suffered a stroke and memory loss before he passed. The last time I visited him, I was in my thirties. He was convinced I was still in college and was so happy I was graduating. That bittersweet memory never left me.

当我担任这部影片的技术负责人时，我就知道这个项目在情感上和技术上都极具挑战性。记忆丧失对我个人而言有着特殊的意义。我的祖父在去世前曾中风并伴有记忆丧失。我最后一次探望他时已经三十多岁了，但他坚信我还在上大学，并为我即将毕业感到非常高兴。那段苦乐参半的记忆一直萦绕在我的心头。

The experience made me want to see if this technology could help me connect more deeply with my family through the same medium. When this project began, I asked my father for old family photos. I tested our image restoration on photographs from when my parents met and used our video models to animate them. Watching my parents move as twenty-somethings really drove home how these tools could help keep what matters most from slipping away.

这段经历让我想要探索，这种技术是否能通过同样的方式帮助我与家人建立更深层的联系。项目开始时，我向父亲要了一些老照片。我利用我们的图像修复技术处理了父母初识时的照片，并使用视频模型让它们动了起来。看着父母在二十多岁时的样子动起来，我深刻地意识到，这些工具确实能帮助我们留住那些最珍贵、最不愿失去的东西。

“Love, Rendered” was directed by Academy Award–nominated filmmaker Liz Garbus, who produced it alongside Dan Cogan and Darren Aronofsky. It was created in collaboration between Google DeepMind and Primordial Soup, Aronofsky’s creative venture.

《Love, Rendered》由奥斯卡提名导演 Liz Garbus 执导，并与 Dan Cogan 和 Darren Aronofsky 共同制作。该片由 Google DeepMind 与 Aronofsky 的创意公司 Primordial Soup 合作完成。

Exploring the mystery of memories

探索记忆的奥秘

Liz and Darren both came to the film curious about the resilience of memory. While directing the documentary “Coma,” Liz saw fMRI scans light up when patients in minimally conscious states heard familiar voices or were shown images of loved ones. Years later, Darren encountered footage of a former ballerina with Alzheimer’s who, upon hearing “Swan Lake,” instinctively danced the choreography from her wheelchair.

Liz 和 Darren 在制作这部影片时，都对记忆的韧性充满好奇。在执导纪录片《Coma》时，Liz 观察到当处于微意识状态的患者听到熟悉的声音或看到亲人的照片时，他们的功能性磁共振成像（fMRI）扫描图会亮起。多年后，Darren 看到了一段患有阿尔茨海默症的前芭蕾舞演员的视频：当她听到《天鹅湖》的音乐时，竟在轮椅上本能地跳起了舞。

These touchstones led the creative team to reminiscence therapy, the clinical practice of using sensory cues like songs, family stories, and old photographs to stimulate memories, spark conversations, and rekindle emotional connections. But what happens when a memory isn’t attached to such cues?

这些触动让创作团队关注到了“怀旧疗法”（Reminiscence Therapy）。这是一种临床实践，通过歌曲、家庭故事和老照片等感官线索来刺激记忆、引发对话并重燃情感联系。但如果一段记忆缺乏这些线索，又该怎么办呢？

To recreate Burt and Ethelle’s unrecorded past, the filmmakers partnered with my team. Sitting alongside us, Ethelle became an active co-creator, correcting the curve of a staircase or the shape of a shoe heel.

为了重现 Burt 和 Ethelle 未被记录的过去，电影制作人与我的团队展开了合作。Ethelle 坐在我们身边，成为了积极的共同创作者，她会纠正楼梯的弧度或鞋跟的形状等细节。

Capturing human essence with AI

用 AI 捕捉人性本质

We worked alongside the team at Primordial Soup, leveraging emerging AI models as an art medium and toolkit, guiding the technology with human direction every step of the way. Bridging the gaps between the missing details required a two-part technical approach designed to preserve emotional truth.

我们与 Primordial Soup 团队并肩工作，将新兴的 AI 模型作为艺术媒介和工具，并在每一步都通过人工引导来驾驭这项技术。填补缺失细节的空白需要一种两阶段的技术方案，旨在保留情感的真实性。

• Image restoration: The team used generative models to restore black-and-white photos of Burt and Ethelle from their youth. The restored photographs helped to ensure that subsequent recreations remained faithful to their subjects.

• 图像修复： 团队使用生成模型修复了 Burt 和 Ethelle 年轻时的黑白照片。修复后的照片确保了后续的重现工作能够忠实于原型。

• Pose and performance control: Engineers used our performance capture models to map Burt and Ethelle’s present-day micro-mannerisms — the specific tilt of Burt’s head, a brief hesitation in his speech pattern, the subtle crinkle around his eyes. Mapping those traits onto their younger likenesses brought the recreation to life.

• 姿态与表演控制： 工程师利用我们的动作捕捉模型，映射了 Burt 和 Ethelle 如今的微表情和习惯动作——比如 Burt 头部特定的倾斜角度、他说话时短暂的停顿，以及他眼角细微的皱纹。将这些特征映射到他们年轻时的形象上，让重现的画面栩栩如生。

Combining these two mediums allowed us to intertwine Burt and Ethelle’s past and their present. In doing so, we generated a “memory” that they told us felt authentic. As Darren noted during production, a tool — like a paintbrush or a hammer — does nothing until it’s guided by human hands. In “Love, Rendered,” machine learning was the tool we used to guide Burt and Ethelle back through time, helping them.

结合这两种媒介，我们将 Burt 和 Ethelle 的过去与现在交织在一起。通过这种方式，我们生成了一段他们认为“真实可信”的记忆。正如 Darren 在制作过程中所指出的，工具（如画笔或锤子）在没有人类引导的情况下毫无作用。在《Love, Rendered》中，机器学习就是我们引导 Burt 和 Ethelle 回溯时光的工具，帮助他们找回了那段珍贵的记忆。