Film Profile · WAKE-UP-DEAD-MAN-A-KNIVES-OUT-MYSTERY
Wake Up Dead Man A Knives Out Mystery
Detections100,491
Night Frames82%
Dialogue WPM88
Dom. Emotionneutral
GENOME
Movie DNA
Six core dimensions, each scored against the genre baseline.
Darkness-59% vs genre
22/100
Bright
Action Intensity-11% vs genre
49/100
Moderate
People Density-7% vs genre
47/100
Intimate
Indoor / Outdoor
89% indoor
Indoor · 89.1% indoors
Object Diversity
72/100
High
Pacing
0.7 cuts/min
Dynamic · cuts/min
FINGERPRINT
The full shape, vs. genre
Six axes overlaid against the average for this film's primary genre.
Wake Up Dead Man A Knives Out MysteryThriller avg · 4 titles
THE ARC
Motion & brightness across the runtime
Hover to scrub. Spikes mark high-action sequences. Dips reveal quiet stretches.
Motion Intensity
Brightness
People
STRUCTURE
Scene mix by act
Three-act split — how the visual tone shifts from setup through climax.
PEAKS & VALLEYS
When the film pushes — and breathes
The single most-intense minute and longest stretch of stillness.
CHROMA
Color signature
Frame-sampled palette — temperature, saturation, and how the look shifts across the runtime.
Temperaturewarm
Saturationmuted
Frames sampled7,021
Film color bandhow the palette shifts across the runtime · 60 buckets
0:00¼½¾end
Overall palette6 dominant colors · % of sampled frames
#52493B
26.0%
#716752
20.8%
#492C1C
20.0%
#8C8670
14.9%
#AEAB92
10.4%
#D1CEC0
7.8%
Palette by scene type5 dominant colors per context · sampled within scene class
Empty scenes
#706451
#49412C
#858072
#A6A48F
#CBC9BA
General scenes
#636054
#4C3A29
#918C75
#C8C5B1
#81663C
Intimate scenes
#4A2E1E
#5E5F59
#755232
#908B78
#C9C7B4
Night scenes
#4A443A
#4E2818
#72634F
#99937B
#CBC9B9
Outdoor scenes
#5F5443
#473324
#7F7864
#A6A28B
#CECCBD
Vibrant scenes
#6C452C
#8E653A
#80371D
#5B783A
#B79F4C
DETECTIONS
What appears on screen most
The four most-present objects — full breakdown with categories in Deep Dive.
ON SCREEN
Cast at a glance
Top 3 actors by screen time, color-coded by dominant emotion.
JO
Josh O'Connor
Fr. Jud Duplenticy
35.7m · neutral
DC
Daniel Craig
Benoit Blanc
24.3m · angry
GC
Glenn Close
Martha Delacroix
12.8m · sad
AI-DISCOVERED FACTS
AI-Discovered Facts — Wake Up Dead Man A Knives Out Mystery (2025)
Insights surfaced by machine analysis of every frame, audio track, and object.
- Wake Up Dead Man is an unexpectedly intimate thriller, spending a staggering 87% of its runtime indoors, outdoing the industry average by a whopping 22%.
- 2,935 unique people spotted (87% of frames)
- "father" is said 59 times — once every 2.5 minutes.
- Josh O'Connor is on screen for 36 of 146 minutes — 24% of the runtime.
- The longest stretch of silence runs 9 min 9 sec — no dialogue at all.
- 48 profanity instances across the runtime — roughly one every 182 seconds.
- Only 6.3% of all detected faces are smiling across the entire film.
- At 134 BPM, the soundtrack rarely lets up — one of the faster-paced scores in the database.
- 3,110 unique words spoken out of 12,915 total — a vocabulary richness of 24.1%.
- AI detected 72 unique object types across 100,491 frame-by-frame detections.
- 736 unique ties spotted (16% of frames)
- 650 unique chairs spotted (11% of frames)
- 365 unique benchs spotted (6% of frames)
- 237 unique couchs spotted (4% of frames)
- 141 unique cups spotted (3% of frames)
- 108 unique potted plants spotted (2% of frames)
- 128 unique bottles spotted (2% of frames)
- 130 unique books spotted (2% of frames)
- 96 unique cars spotted (2% of frames)
- Features 233 dog appearances
- Contains 48 profanities (0.3 per minute)
- Longest silence: 9 minutes of unbroken quiet
- 28% of frames are close-ups
- The film's emotional journey ends on a neutral note
- Shot predominantly with warm tones
DIALOGUE
Words across the runtime
Volume and pace at a glance. Top vocabulary and per-act WPM in Deep Dive.
48% dialogue52% silence / score
12,915
total words
88
words / min
48 · 0.4%
profanity flagged
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